Project 1: Explore and Prepare Data
CSE6242 - Data and Visual Analytics - Fall 2017
Due: Sunday, October 15, 2017 at 11:59 PM UTC-12:00 on T-Square
Vincent La (Georgia Tech ID - vla6)
October 12, 2017
Note: This project involves getting data ready for analysis and doing some preliminary investigations. Project 2 will involve modeling and predictions on the same dataset, and will be released at a later date. Both projects will have equal weightage towards your grade. You may reuse some of the preprocessing/analysis steps from Project 1 in Project 2.
Data
In this project, you will explore a dataset that contains information about movies, including ratings, budget, gross revenue and other attributes. It was prepared by Dr. Guy Lebanon, and here is his description of the dataset:
The file
movies_mergedcontains a dataframe with the same name that has 40K rows and 39 columns. Each row represents a movie title and each column represents a descriptor such asTitle,Actors, andBudget. I collected the data by querying IMDb’s API (see www.omdbapi.com) and joining it with a separate dataset of movie budgets and gross earnings (unknown to you). The join key was the movie title. This data is available for personal use, but IMDb’s terms of service do not allow it to be used for commercial purposes or for creating a competing repository.
Objective
Your goal is to investigate the relationship between the movie descriptors and the box office success of movies, as represented by the variable Gross. This task is extremely important as it can help a studio decide which titles to fund for production, how much to bid on produced movies, when to release a title, how much to invest in marketing and PR, etc. This information is most useful before a title is released, but it is still very valuable after the movie is already released to the public (for example it can affect additional marketing spend or how much a studio should negotiate with on-demand streaming companies for “second window” streaming rights).
Instructions
This is an R Markdown Notebook. Open this file in RStudio to get started.
When you execute code within the notebook, the results appear beneath the code. Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Cmd+Shift+Enter.
# Project 1: http://cse6242.gatech.edu/fall-2017/pr1/
# To compile R Markdown in terminal run: Rscript -e "rmarkdown::render('pr1.Rmd', clean=TRUE)"
x = 1:10
print(x^2) [1] 1 4 9 16 25 36 49 64 81 100Plots appear inline too:
plot(x, x^2, 'o')
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I. Enter some R code and run it.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).
Please complete all the tasks below by implementing code chunks that have a TODO comment in them, running all code chunks so that output and plots are displayed, and typing in answers to each question (Q: …) next to/below the corresponding answer prompt (A:). Feel free to add code chunks/show additional output to support any of the answers.
When you are done, you will need to submit the final R markdown file (as pr1.Rmd) with all code chunks implemented and executed, and all text responses written in. You also need to submit a PDF export of the markdown file (as pr1.pdf), which should show your code, output, plots and written responses–this will be your project report. Compress these two files into a single .zip archive and upload it on T-Square.
Setup
Load data
Make sure you’ve downloaded the movies_merged file and it is in the current working directory. Now load it into memory:
load('movies_merged')
cat("Dataset has", dim(movies_merged)[1], "rows and", dim(movies_merged)[2], "columns", end="\n", file="")Dataset has 40789 rows and 39 columns This creates an object of the same name (movies_merged). For convenience, you can copy it to df and start using it:
df = movies_merged
cat("Column names:", end="\n", file="")Column names: colnames(df) [1] "Title" "Year" "Rated"
[4] "Released" "Runtime" "Genre"
[7] "Director" "Writer" "Actors"
[10] "Plot" "Language" "Country"
[13] "Awards" "Poster" "Metascore"
[16] "imdbRating" "imdbVotes" "imdbID"
[19] "Type" "tomatoMeter" "tomatoImage"
[22] "tomatoRating" "tomatoReviews" "tomatoFresh"
[25] "tomatoRotten" "tomatoConsensus" "tomatoUserMeter"
[28] "tomatoUserRating" "tomatoUserReviews" "tomatoURL"
[31] "DVD" "BoxOffice" "Production"
[34] "Website" "Response" "Budget"
[37] "Domestic_Gross" "Gross" "Date" Title
Year
Rated
Released
Runtime
Genre
Director
Writer
Actors
Plot
Language
Country
Awards
Poster
Metascore
imdbRating
imdbVotes
imdbID
Type
tomatoMeter
tomatoImage
tomatoRating
tomatoReviews
tomatoFresh
tomatoRotten
tomatoConsensus
tomatoUserMeter
tomatoUserRating
tomatoUserReviews
tomatoURL
DVD
BoxOffice
Production
Website
Response
Budget
Domestic_Gross
Gross
DateLoad R packages
Load any R packages that you will need to use. You can come back to this chunk, edit it and re-run to load any additional packages later.
library(ggplot2)
library(GGally)
# Note that I'm using Anaconda R, and I was having trouble installing qdapTools. I'm not sure
# if this is the same on other machines but I did have to install R XML separately
# https://anaconda.org/r/r-xml
# conda install -c r r-xml. Only after could I install qdapTools.
library(qdapTools)If you are loading any non-standard packages (ones that have not been discussed in class or explicitly allowed for this project), please mention them below. Include any special instructions if they cannot be installed using the regular install.packages('<pkg name>') command.
Non-standard packages used: None
Tasks
Each task below is worth 10 points, and is meant to be performed sequentially, i.e. do step 2 after you have processed the data as described in step 1. Total points: 100
Complete each task by implementing code chunks as described by TODO comments, and by responding to questions (“Q:”) with written answers (“A:”). If you are unable to find a meaningful or strong relationship in any of the cases when requested, explain why not by referring to appropriate plots/statistics.
It is okay to handle missing values below by omission, but please omit as little as possible. It is worthwhile to invest in reusable and clear code as you may need to use it or modify it in project 2.
1. Remove non-movie rows
The variable Type captures whether the row is a movie, a TV series, or a game. Remove all rows from df that do not correspond to movies.
# TODO: Remove all rows from df that do not correspond to movies
df2 <- df[df$Type == "movie",]
original_dim = dim(df)
new_dim = dim(df2)
df = df2
# Differences in rows
print(original_dim[1] - new_dim[1])[1] 789Q: How many rows are left after removal? Enter your response below.
A: The number of rows left after removal are 40000.
2. Process Runtime column
The variable Runtime represents the length of the title as a string. Write R code to convert it to a numeric value (in minutes) and replace df$Runtime with the new numeric column.
extract_runtime = function(r){
times = unlist(r)
minutes = 0
for (i in 1:length(times) - 1){
if (times[i + 1] == 'h'){
minutes = minutes + as.numeric(times[i]) * 60
} else if (times[i + 1] == 'min'){
minutes = minutes + as.numeric(times[i])
}
}
if (minutes == 0){
return(NA)
} else{
return(minutes)
}
}
y=strsplit(df$Runtime,' ')
new_runtimes = unlist(lapply(y, extract_runtime))
df$Runtime = new_runtimes
cols = c('Runtime', 'Year', 'Budget')
summary(df[cols]) Runtime Year Budget
Min. : 1.00 Min. :1888 Min. : 1100
1st Qu.: 72.00 1st Qu.:1961 1st Qu.: 5000000
Median : 90.00 Median :1989 Median : 18000000
Mean : 81.79 Mean :1981 Mean : 31661108
3rd Qu.:101.00 3rd Qu.:2001 3rd Qu.: 40000000
Max. :873.00 Max. :2018 Max. :425000000
NA's :751 NA's :35442 Now investigate the distribution of Runtime values and how it changes over years (variable Year, which you can bucket into decades) and in relation to the budget (variable Budget). Include any plots that illustrate.
# TODO: Investigate the distribution of Runtime values and how it varies by Year and Budget
ggplot(df, aes(x=Runtime)) +
geom_histogram() +
ggtitle('Histogram of Runtime (minutes)')`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 751 rows containing non-finite values (stat_bin).
ggplot(df, aes(Year, Runtime)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 751 rows containing non-finite values (stat_summary).
ggplot(df, aes(Budget, Runtime)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 35480 rows containing non-finite values (stat_summary).
Feel free to insert additional code chunks as necessary.
Q: Comment on the distribution as well as relationships. Are there any patterns or trends that you can observe?
A:
Looking in the aggregate, it seems like most runtimes are under 2 hours. There is a clear relationship between run time and year, looking a bit like a sigmoid function. This makes sense as very early years, it was probably technologically prohibitive to create long movies. As technology improved, runtimes increased, but there’s only so much time you can expect to hold an audience captive. Interestingly enough, during the 1990’s there seems to be a downward trend in runtimes until around mid 2000’s where it picked up again.
3. Encode Genre column
The column Genre represents a list of genres associated with the movie in a string format. Write code to parse each text string into a binary vector with 1s representing the presence of a genre and 0s the absence, and add it to the dataframe as additional columns. Then remove the original Genre column.
For example, if there are a total of 3 genres: Drama, Comedy, and Action, a movie that is both Action and Comedy should be represented by a binary vector <0, 1, 1>. Note that you need to first compile a dictionary of all possible genres and then figure out which movie has which genres (you can use the R tm package to create the dictionary).
# TODO: Replace Genre with a collection of binary columns
require(tm)Loading required package: tmWarning in library(package, lib.loc = lib.loc, character.only = TRUE,
logical.return = TRUE, : there is no package called 'tm'# All unique genres in the dataframe
print(unique(unlist(strsplit(df$Genre, ', ')))) [1] "Documentary" "Biography" "Romance" "Short" "Thriller"
[6] "Drama" "War" "Comedy" "Horror" "Sci-Fi"
[11] "Adventure" "Family" "History" "Crime" "Action"
[16] "Music" "Mystery" "Fantasy" "Sport" "Animation"
[21] "Musical" "N/A" "Talk-Show" "Adult" "Western"
[26] "Film-Noir" "Reality-TV" "News" "Game-Show" # Example of how to one-hot encode: https://stackoverflow.com/questions/39778387/r-dataframe-one-hot-encoding-of-column-containing-multiple-terms
df = cbind(df, mtabulate(strsplit(df$Genre, ", ")))
cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')
# Remove column from dataframe
df$Genre = NULLPlot the relative proportions of movies having the top 10 most common genres.
# TODO: Select movies from top 10 most common genres and plot their relative proportions
# See https://stackoverflow.com/questions/20679702/r-find-column-with-the-largest-column-sum as reference
cols = setdiff(cols, 'Genre')
colCount = colSums(df[cols])
topTenIds = order(colCount,decreasing=TRUE)[1:10] + 1
topTenCols = names(df[cols][topTenIds])
# The top 10 most common genres are
print(topTenCols) [1] "Family" "Crime" "Sport" "Sci-Fi" "Adult"
[6] "Documentary" "War" "Drama" "Animation" "Biography" # Probably need to do some manual "stacking" of dataframes by each of the top 10 most common genres.
# Note that I was somewhat confused on what "relative proportions of movies" meant. I found this helpful
# https://piazza.com/class/j6gt7ycx6nk145?cid=396
# Suppose you have ten ice creams. Let's say that four of them are Chocolate ice cream. What is the relative proportion of Chocolate ice cream ? --> 4 / 10
cols3 = c('Title', 'Runtime', topTenCols)
df3 = df[cols3]
print(summary(df3)) Title Runtime Family Crime
Length:40000 Min. : 1.00 Min. :0.00000 Min. :0.0000
Class :character 1st Qu.: 72.00 1st Qu.:0.00000 1st Qu.:0.0000
Mode :character Median : 90.00 Median :0.00000 Median :0.0000
Mean : 81.79 Mean :0.06632 Mean :0.1016
3rd Qu.:101.00 3rd Qu.:0.00000 3rd Qu.:0.0000
Max. :873.00 Max. :1.00000 Max. :1.0000
NA's :751
Sport Sci-Fi Adult Documentary
Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.0000 Median :0.00000 Median :0.00000
Mean :0.01312 Mean :0.0406 Mean :0.01055 Mean :0.07627
3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.00000
War Drama Animation Biography
Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.00000 Median :0.0000 Median :0.0000 Median :0.00000
Mean :0.02675 Mean :0.3965 Mean :0.0697 Mean :0.02772
3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
print(head(df3)) Title Runtime Family Crime Sport Sci-Fi Adult Documentary
1 39 Pounds of Love 70 0 0 0 0 0 1
2 3:am 21 0 0 0 0 0 0
3 500 Years Later 106 0 0 0 0 0 1
4 5th World 75 0 0 0 0 0 0
5 90 14 0 0 0 0 0 0
6 Abel Raises Cain 82 0 0 0 0 0 1
War Drama Animation Biography
1 0 0 0 1
2 0 0 0 0
3 0 0 0 0
4 0 1 0 0
5 1 0 0 0
6 0 0 0 1library(reshape2)
df3.long = melt(df3, id.vars=c('Title', 'Runtime'))
# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df3.long = df3.long[apply(df3.long['value'],1,function(z) !any(z==0)),]
print(summary(df3.long)) Title Runtime variable value
Length:33163 Min. : 1.00 Drama :15859 Min. :1
Class :character 1st Qu.: 73.00 Crime : 4062 1st Qu.:1
Mode :character Median : 91.00 Documentary: 3051 Median :1
Mean : 83.88 Animation : 2788 Mean :1
3rd Qu.:105.00 Family : 2653 3rd Qu.:1
Max. :873.00 Sci-Fi : 1624 Max. :1
NA's :407 (Other) : 3126 print(head(df3.long)) Title Runtime variable value
11 Aliens of the Deep 100 Family 1
21 Are We There Yet? 95 Family 1
29 Because of Winn-Dixie 106 Family 1
64 Down and Derby 90 Family 1
68 Duma 100 Family 1
73 Eliana em O Segredo dos Golfinhos 94 Family 1# Finally plot relative proportions
# https://sebastiansauer.github.io/percentage_plot_ggplot2_V2/
ggplot(df3.long, aes(x=variable)) +
geom_bar(aes(y = (..count..)/sum(..count..))) +
ylab('Relative Proportion')
Examine how the distribution of Runtime changes across genres for the top 10 most common genres.
# TODO: Plot Runtime distribution for top 10 most common genres
# ggplot(df3.long, aes(x=Runtime)) +
# geom_density(aes(color=variable, group=variable)) +
# ggtitle('Distribution of Runtimes by Movie Genre')
ggplot(df3.long, aes(as.factor(variable), Runtime)) +
geom_boxplot() +
coord_flip() +
scale_x_discrete("Genre")Warning: Removed 407 rows containing non-finite values (stat_boxplot).
Q: Describe the interesting relationship(s) you observe. Are there any expected or unexpected trends that are evident?
A: One thing that immediately stood out to me was that in looking at the extreme outliers (Runtimes greater than 300 minutes for example or 5 hours), most of these very long movies tend to be Drama, War, and Documentaries, which fits anecdotally with my priors of what movies would be extremely long.
Animation films tend to be the shortest, which is expected as there are probably many short animation films. I was actually surprised to see that most animation films are very very short.
One that that was unexpected to me was that with the exception of Animation and Family films, most of the median runtimes for all the rest of the genres are very similar.
4. Eliminate mismatched rows
The dataframe was put together by merging two different sources of data and it is possible that the merging process was inaccurate in some cases (the merge was done based on movie title, but there are cases of different movies with the same title). There are 3 columns that contain date information: Year (numeric year), Date (numeric year), and Released (string representation of the release date).
Find and remove all rows where you suspect a merge error occurred based on a mismatch between these variables. To make sure subsequent analysis and modeling work well, avoid removing more than 10% of the rows that have a Gross value present.
Note: Do not remove the rows with Gross == NA at this point, just use this a guideline.
# TODO: Remove rows with Year/Date/Released mismatch
library(lubridate) # Used to extract year from ReleasedLoading required package: methods
Attaching package: 'lubridate'The following object is masked from 'package:base':
datecols4 = c('Year', 'Date', 'Released', 'Gross')
df4 = df[cols4]
df4$released_year = year(df4$Released)
df4$not_matched = (df4$Year != df4$released_year) & (df4$Year != df4$Date) & (df4$Date != df4$released_year)
# Number of rows with non-null Gross Value
print(nrow(df[which(!is.na(df$Gross)), ]))[1] 4558# Printing rows in the original data set
print(nrow(df))[1] 40000df = df[which(df4$not_matched == FALSE | is.na(df4$not_matched)), ]
print(nrow(df))[1] 39952Q: What is your precise removal logic, and how many rows remain in the resulting dataset?
A: Out of the 40,000 original rows, I removed rows where Year != released_year and Year != Date and Date != released_year This resulted in 48 rows to be removed where there was a non-null Gross value. Since there were originally 4510 rows with a non-null Gross value, we have indeed removed less than 10 percent of the rows with non-null Gross values.
There are now 39952 rows in the resulting dataset.
5. Explore Gross revenue
For the commercial success of a movie, production houses want to maximize Gross revenue. Investigate if Gross revenue is related to Budget, Runtime or Genre in any way.
Note: To get a meaningful relationship, you may have to partition the movies into subsets such as short vs. long duration, or by genre, etc.
# TODO: Investigate if Gross Revenue is related to Budget, Runtime or Genre
cols = c('Title', 'Gross', 'Runtime', 'Budget', 'Released', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')
df5 = df[cols]
print(summary(df5)) Title Gross Runtime
Length:39952 Min. :0.000e+00 Min. : 1.00
Class :character 1st Qu.:5.000e+06 1st Qu.: 72.00
Mode :character Median :3.006e+07 Median : 90.00
Mean :9.057e+07 Mean : 81.78
3rd Qu.:1.008e+08 3rd Qu.:101.00
Max. :2.784e+09 Max. :873.00
NA's :35442 NA's :751
Budget Released Action
Min. : 1100 Min. :1893-05-09 Min. :0.0000
1st Qu.: 5000000 1st Qu.:1959-02-04 1st Qu.:0.0000
Median : 18000000 Median :1989-10-06 Median :0.0000
Mean : 31952913 Mean :1980-12-02 Mean :0.1104
3rd Qu.: 40000000 3rd Qu.:2001-11-07 3rd Qu.:0.0000
Max. :425000000 Max. :2018-08-01 Max. :1.0000
NA's :35442 NA's :4949
Adult Adventure Animation Biography
Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.01056 Mean :0.07324 Mean :0.06976 Mean :0.02768
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
Comedy Crime Documentary Drama
Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
Mean :0.3212 Mean :0.1014 Mean :0.07624 Mean :0.3963
3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
Family Fantasy Film-Noir Game-Show
Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00e+00
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00e+00
Median :0.00000 Median :0.00000 Median :0.000000 Median :0.00e+00
Mean :0.06638 Mean :0.03502 Mean :0.008811 Mean :5.01e-05
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00e+00
Max. :1.00000 Max. :1.00000 Max. :1.000000 Max. :1.00e+00
History Horror Music Musical
Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.02075 Mean :0.06781 Mean :0.02961 Mean :0.03469
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
Mystery N/A News
Min. :0.00000 Min. :0.00000 Min. :0.0000000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000000
Median :0.00000 Median :0.00000 Median :0.0000000
Mean :0.04097 Mean :0.02468 Mean :0.0005006
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000000
Max. :1.00000 Max. :1.00000 Max. :1.0000000
Reality-TV Romance Sci-Fi Short
Min. :0.0000000 Min. :0.0000 Min. :0.00000 Min. :0.0000
1st Qu.:0.0000000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.0000000 Median :0.0000 Median :0.00000 Median :0.0000
Mean :0.0001502 Mean :0.1243 Mean :0.04052 Mean :0.1631
3rd Qu.:0.0000000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
Max. :1.0000000 Max. :1.0000 Max. :1.00000 Max. :1.0000
Sport Talk-Show Thriller War
Min. :0.00000 Min. :0.0000000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.0000000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.0000000 Median :0.00000 Median :0.00000
Mean :0.01314 Mean :0.0001001 Mean :0.08438 Mean :0.02676
3rd Qu.:0.00000 3rd Qu.:0.0000000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :1.0000000 Max. :1.00000 Max. :1.00000
Western
Min. :0.00000
1st Qu.:0.00000
Median :0.00000
Mean :0.03289
3rd Qu.:0.00000
Max. :1.00000
print(head(df5)) Title Gross Runtime Budget Released Action Adult Adventure
1 39 Pounds of Love NA 70 NA 2005-04-08 0 0 0
2 3:am NA 21 NA 2005-01-25 0 0 0
3 500 Years Later NA 106 NA 2005-02-24 0 0 0
4 5th World NA 75 NA 2005-01-20 0 0 0
5 90 NA 14 NA 2005-03-12 0 0 0
6 Abel Raises Cain NA 82 NA 2005-01-23 0 0 0
Animation Biography Comedy Crime Documentary Drama Family Fantasy
1 0 1 0 0 1 0 0 0
2 0 0 0 0 0 0 0 0
3 0 0 0 0 1 0 0 0
4 0 0 0 0 0 1 0 0
5 0 0 0 0 0 0 0 0
6 0 1 0 0 1 0 0 0
Film-Noir Game-Show History Horror Music Musical Mystery N/A News
1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0 0 0
Reality-TV Romance Sci-Fi Short Sport Talk-Show Thriller War Western
1 0 1 0 0 0 0 0 0 0
2 0 0 0 1 0 0 1 0 0
3 0 0 0 0 0 0 0 0 0
4 0 1 0 0 0 0 0 0 0
5 0 0 0 1 0 0 0 1 0
6 0 0 0 0 0 0 0 0 0ggplot(df, aes(Runtime, Gross)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 35480 rows containing non-finite values (stat_summary).
ggplot(df, aes(Budget, Gross)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 35442 rows containing non-finite values (stat_summary).
# Next melt the df to get genre as one column (like in question 3) and plot grouped by genre
df5.long = melt(df5, id.vars=c('Title', 'Gross', 'Runtime', 'Budget', 'Released'), variable.name='genre')
# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df5.long = df5.long[apply(df5.long['value'], 1, function(z) !any(z==0)),]
library(plyr)
Attaching package: 'plyr'The following object is masked from 'package:lubridate':
hereThe following object is masked from 'package:qdapTools':
iddf5.long.runtime <- ddply(df5.long, c('Runtime', 'genre'), function(x) mean(x$Gross))
names(df5.long.runtime)[names(df5.long.runtime) == 'V1'] <- 'Gross'
df5.long.runtime = df5.long.runtime[!df5.long.runtime$Runtime > 180,] # Removing movies greater than 3 hours
df5.long.budget <- ddply(df5.long, c('Budget', 'genre'), function(x) mean(x$Gross))
names(df5.long.budget)[names(df5.long.budget) == 'V1'] <- 'Gross'
ggplot(df5.long.runtime, aes(Runtime, Gross)) +
geom_point(aes(color=genre, group=genre))Warning: Removed 3327 rows containing missing values (geom_point).
ggplot(df5.long.budget, aes(Budget, Gross)) +
geom_point(aes(color=genre, group=genre))Warning: Removed 29 rows containing missing values (geom_point).
Q: Did you find any observable relationships or combinations of Budget/Runtime/Genre that result in high Gross revenue? If you divided the movies into different subsets, you may get different answers for them - point out interesting ones.
A:
Yes, it seems that in the aggregate, Gross revenue is positive correlated with Runtime, but only to a certain extent. After about 3 hours or so, the relationship is much murkier. Gross Revenue seems generally positively correlated with Budget.
# TODO: Investigate if Gross Revenue is related to Release Month
print(summary(df5.long)) Title Gross Runtime
Length:79959 Min. :0.000e+00 Min. : 1.00
Class :character 1st Qu.:6.882e+06 1st Qu.: 69.00
Mode :character Median :3.457e+07 Median : 90.00
Mean :9.891e+07 Mean : 81.15
3rd Qu.:1.106e+08 3rd Qu.:102.00
Max. :2.784e+09 Max. :873.00
NA's :68815 NA's :1099
Budget Released genre value
Min. : 1100 Min. :1893-05-09 Drama :15832 Min. :1
1st Qu.: 7000000 1st Qu.:1956-08-16 Comedy :12833 1st Qu.:1
Median : 20000000 Median :1989-04-28 Short : 6516 Median :1
Mean : 35134828 Mean :1980-04-16 Romance: 4966 Mean :1
3rd Qu.: 48000000 3rd Qu.:2001-12-13 Action : 4409 3rd Qu.:1
Max. :425000000 Max. :2018-08-01 Crime : 4053 Max. :1
NA's :68815 NA's :8537 (Other):31350 print(head(df5.long)) Title Gross Runtime Budget Released genre value
23 Assault on Precinct 13 NA 109 NA 2005-01-19 Action 1
39 Boy s tenyu NA 132 NA 2005-03-17 Action 1
41 Broadcast 23 NA 7 NA <NA> Action 1
47 Chok-Dee NA 105 NA 2005-02-16 Action 1
48 Choker NA 93 NA 2006-10-10 Action 1
69 Dust to Glory NA 97 NA 2005-04-22 Action 1df$released_month = month(df$Released)
ggplot(df, aes(released_month, Gross)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 35487 rows containing non-finite values (stat_summary).
There is definitely a relationship of Gross Revenue and release month. Summer month movies and holiday movies gross significantly more revenue than movies released in other months.
6. Process Awards column
The variable Awards describes nominations and awards in text format. Convert it to 2 numeric columns, the first capturing the number of wins, and the second capturing nominations. Replace the Awards column with these new columns, and then study the relationship of Gross revenue with respect to them.
Note: The format of the Awards column is not standard; you may have to use regular expressions to find the relevant values. Try your best to process them, and you may leave the ones that don’t have enough information as NAs or set them to 0s.
# TODO: Convert Awards to 2 numeric columns: wins and nominations
library(stringi)
cols = c('Title', 'Awards', 'Gross')
df6 = df[cols]
print(unique(df6$Awards)) [1] "3 wins."
[2] "N/A"
[3] "2 wins."
[4] "1 win."
[5] "5 wins."
[6] "5 wins & 4 nominations."
[7] "1 win & 1 nomination."
[8] "2 wins & 1 nomination."
[9] "1 nomination."
[10] "1 win & 8 nominations."
[11] "1 win & 2 nominations."
[12] "1 win & 13 nominations."
[13] "15 nominations."
[14] "2 nominations."
[15] "28 wins & 3 nominations."
[16] "10 nominations."
[17] "4 wins & 1 nomination."
[18] "6 wins."
[19] "3 wins & 15 nominations."
[20] "2 wins & 9 nominations."
[21] "1 win & 5 nominations."
[22] "13 wins & 13 nominations."
[23] "4 wins."
[24] "Nominated for 1 Oscar. Another 3 wins & 10 nominations."
[25] "4 wins & 7 nominations."
[26] "4 wins & 3 nominations."
[27] "9 nominations."
[28] "3 wins & 2 nominations."
[29] "2 wins & 3 nominations."
[30] "16 nominations."
[31] "10 wins & 13 nominations."
[32] "4 nominations."
[33] "6 nominations."
[34] "4 wins & 24 nominations."
[35] "5 wins & 10 nominations."
[36] "5 wins & 2 nominations."
[37] "7 nominations."
[38] "4 wins & 13 nominations."
[39] "1 win & 3 nominations."
[40] "21 wins & 15 nominations."
[41] "2 wins & 5 nominations."
[42] "1 win & 10 nominations."
[43] "3 wins & 4 nominations."
[44] "3 nominations."
[45] "4 wins & 8 nominations."
[46] "Nominated for 1 Oscar. Another 13 wins & 15 nominations."
[47] "Nominated for 1 BAFTA Film Award. Another 6 wins & 3 nominations."
[48] "6 wins & 5 nominations."
[49] "2 wins & 19 nominations."
[50] "12 wins & 1 nomination."
[51] "31 wins & 47 nominations."
[52] "Won 1 Oscar. Another 5 wins & 2 nominations."
[53] "7 wins & 12 nominations."
[54] "4 wins & 5 nominations."
[55] "Nominated for 2 Oscars. Another 31 wins & 22 nominations."
[56] "Won 1 Oscar. Another 7 wins & 1 nomination."
[57] "4 wins & 2 nominations."
[58] "3 wins & 24 nominations."
[59] "10 wins & 2 nominations."
[60] "3 wins & 1 nomination."
[61] "11 nominations."
[62] "32 wins & 65 nominations."
[63] "6 wins & 9 nominations."
[64] "6 wins & 1 nomination."
[65] "6 wins & 10 nominations."
[66] "15 wins & 6 nominations."
[67] "3 wins & 12 nominations."
[68] "Won 1 Golden Globe. Another 5 wins & 7 nominations."
[69] "Nominated for 1 Primetime Emmy. Another 1 win & 1 nomination."
[70] "1 win & 11 nominations."
[71] "Nominated for 1 Oscar. Another 7 wins & 8 nominations."
[72] "10 wins."
[73] "5 wins & 6 nominations."
[74] "2 wins & 10 nominations."
[75] "Nominated for 1 Oscar. Another 9 wins & 27 nominations."
[76] "Nominated for 1 Oscar. Another 8 wins & 18 nominations."
[77] "2 wins & 2 nominations."
[78] "Nominated for 1 Golden Globe. Another 4 nominations."
[79] "Nominated for 1 Golden Globe. Another 2 wins & 16 nominations."
[80] "Nominated for 5 Oscars. Another 8 wins & 26 nominations."
[81] "1 win & 4 nominations."
[82] "Won 1 Oscar. Another 20 wins & 5 nominations."
[83] "13 wins & 9 nominations."
[84] "5 wins & 1 nomination."
[85] "Nominated for 1 Golden Globe. Another 7 nominations."
[86] "8 wins & 17 nominations."
[87] "8 wins & 9 nominations."
[88] "3 wins & 3 nominations."
[89] "9 wins & 7 nominations."
[90] "13 wins & 4 nominations."
[91] "8 wins & 6 nominations."
[92] "2 wins & 6 nominations."
[93] "9 wins."
[94] "11 wins & 2 nominations."
[95] "5 nominations."
[96] "6 wins & 8 nominations."
[97] "Nominated for 1 Golden Globe. Another 1 win & 2 nominations."
[98] "Nominated for 1 BAFTA Film Award. Another 8 wins & 3 nominations."
[99] "Nominated for 2 Oscars. Another 22 wins & 64 nominations."
[100] "9 wins & 9 nominations."
[101] "Won 3 Oscars. Another 61 wins & 105 nominations."
[102] "5 wins & 5 nominations."
[103] "Nominated for 1 Oscar. Another 14 wins & 4 nominations."
[104] "1 win & 16 nominations."
[105] "Nominated for 2 Golden Globes. Another 1 win & 11 nominations."
[106] "9 wins & 4 nominations."
[107] "Won 3 Primetime Emmys. Another 1 win & 2 nominations."
[108] "2 wins & 7 nominations."
[109] "11 wins & 22 nominations."
[110] "13 wins."
[111] "7 wins & 1 nomination."
[112] "6 wins & 3 nominations."
[113] "4 wins & 12 nominations."
[114] "18 wins & 3 nominations."
[115] "Won 1 Oscar. Another 67 wins & 103 nominations."
[116] "Nominated for 1 Oscar. Another 5 wins."
[117] "29 wins & 15 nominations."
[118] "5 wins & 3 nominations."
[119] "7 wins & 2 nominations."
[120] "Won 1 Oscar. Another 20 wins & 77 nominations."
[121] "8 wins & 15 nominations."
[122] "4 wins & 4 nominations."
[123] "Won 2 Oscars. Another 72 wins & 57 nominations."
[124] "13 wins & 37 nominations."
[125] "Nominated for 1 Oscar. Another 1 win & 1 nomination."
[126] "Nominated for 1 Oscar. Another 4 wins."
[127] "Nominated for 1 Oscar. Another 1 nomination."
[128] "Nominated for 2 Oscars. Another 14 wins & 42 nominations."
[129] "2 wins & 4 nominations."
[130] "3 wins & 22 nominations."
[131] "Nominated for 1 Golden Globe. Another 3 nominations."
[132] "Nominated for 1 Golden Globe. Another 2 nominations."
[133] "Nominated for 3 Oscars. Another 16 wins & 42 nominations."
[134] "13 wins & 24 nominations."
[135] "3 wins & 10 nominations."
[136] "Nominated for 1 Oscar. Another 1 win & 13 nominations."
[137] "19 wins & 5 nominations."
[138] "Nominated for 1 Primetime Emmy. Another 2 wins & 5 nominations."
[139] "12 wins & 6 nominations."
[140] "6 wins & 11 nominations."
[141] "Won 1 Oscar. Another 13 wins & 30 nominations."
[142] "11 wins & 9 nominations."
[143] "1 win & 6 nominations."
[144] "Won 1 Primetime Emmy. Another 2 nominations."
[145] "20 nominations."
[146] "Nominated for 2 Golden Globes. Another 21 wins & 72 nominations."
[147] "Nominated for 1 Oscar. Another 17 wins & 48 nominations."
[148] "Nominated for 1 Oscar. Another 5 wins & 1 nomination."
[149] "12 wins & 5 nominations."
[150] "8 wins & 34 nominations."
[151] "1 win & 7 nominations."
[152] "3 wins & 6 nominations."
[153] "Nominated for 1 Oscar. Another 40 wins & 32 nominations."
[154] "7 wins & 23 nominations."
[155] "6 wins & 18 nominations."
[156] "1 win & 9 nominations."
[157] "Won 1 Oscar. Another 2 wins."
[158] "Won 4 Oscars. Another 60 wins & 81 nominations."
[159] "Nominated for 1 BAFTA Film Award. Another 12 wins & 13 nominations."
[160] "8 wins & 1 nomination."
[161] "9 wins & 19 nominations."
[162] "5 wins & 8 nominations."
[163] "10 wins & 22 nominations."
[164] "3 wins & 9 nominations."
[165] "6 wins & 4 nominations."
[166] "Nominated for 1 BAFTA Film Award. Another 1 nomination."
[167] "5 wins & 9 nominations."
[168] "2 wins & 8 nominations."
[169] "13 wins & 7 nominations."
[170] "3 wins & 14 nominations."
[171] "6 wins & 6 nominations."
[172] "8 wins."
[173] "20 wins & 15 nominations."
[174] "11 wins & 1 nomination."
[175] "12 wins."
[176] "7 wins & 4 nominations."
[177] "7 wins."
[178] "3 wins & 5 nominations."
[179] "3 wins & 7 nominations."
[180] "8 wins & 2 nominations."
[181] "9 wins & 1 nomination."
[182] "Nominated for 1 Golden Globe. Another 12 wins & 7 nominations."
[183] "Nominated for 1 Golden Globe. Another 4 wins & 1 nomination."
[184] "8 nominations."
[185] "Won 1 Oscar. Another 26 wins & 2 nominations."
[186] "7 wins & 8 nominations."
[187] "8 wins & 10 nominations."
[188] "Nominated for 3 BAFTA Film Awards. Another 2 wins & 2 nominations."
[189] "Nominated for 1 Oscar. Another 3 wins & 15 nominations."
[190] "13 wins & 20 nominations."
[191] "Nominated for 2 Oscars. Another 16 wins & 48 nominations."
[192] "Won 1 Oscar. Another 120 wins & 82 nominations."
[193] "Nominated for 1 Oscar. Another 1 win."
[194] "7 wins & 17 nominations."
[195] "41 wins & 14 nominations."
[196] "Nominated for 1 Golden Globe. Another 5 wins & 10 nominations."
[197] "Won 1 Oscar. Another 23 wins & 54 nominations."
[198] "14 wins & 20 nominations."
[199] "Nominated for 1 Oscar. Another 7 wins & 11 nominations."
[200] "12 wins & 23 nominations."
[201] "11 wins."
[202] "Nominated for 2 Oscars. Another 7 nominations."
[203] "6 wins & 24 nominations."
[204] "Nominated for 1 Oscar. Another 3 wins & 19 nominations."
[205] "Won 3 Primetime Emmys. Another 1 win & 5 nominations."
[206] "10 wins & 4 nominations."
[207] "2 wins & 17 nominations."
[208] "21 wins & 29 nominations."
[209] "4 wins & 6 nominations."
[210] "Nominated for 3 Oscars. Another 40 wins & 37 nominations."
[211] "4 wins & 10 nominations."
[212] "10 wins & 1 nomination."
[213] "22 wins & 5 nominations."
[214] "6 wins & 7 nominations."
[215] "6 wins & 2 nominations."
[216] "12 wins & 14 nominations."
[217] "3 wins & 13 nominations."
[218] "Nominated for 2 Oscars. Another 29 wins & 71 nominations."
[219] "3 wins & 18 nominations."
[220] "Nominated for 1 Oscar. Another 1 win & 5 nominations."
[221] "Nominated for 1 Oscar. Another 31 wins & 47 nominations."
[222] "Won 3 Oscars. Another 80 wins & 121 nominations."
[223] "12 wins & 2 nominations."
[224] "5 wins & 12 nominations."
[225] "Nominated for 1 Golden Globe. Another 1 win & 11 nominations."
[226] "10 wins & 11 nominations."
[227] "Nominated for 1 Oscar. Another 1 win & 62 nominations."
[228] "15 wins & 4 nominations."
[229] "Nominated for 3 Oscars. Another 3 wins."
[230] "Nominated for 1 Oscar. Another 2 wins & 19 nominations."
[231] "Nominated for 1 BAFTA Film Award. Another 1 win."
[232] "Nominated for 1 Golden Globe. Another 2 wins & 14 nominations."
[233] "Nominated for 1 Oscar. Another 24 wins & 13 nominations."
[234] "13 wins & 26 nominations."
[235] "Nominated for 1 Oscar. Another 7 wins & 7 nominations."
[236] "Won 1 Oscar. Another 17 wins & 92 nominations."
[237] "13 wins & 2 nominations."
[238] "5 wins & 17 nominations."
[239] "Won 1 Oscar. Another 12 wins & 6 nominations."
[240] "21 wins & 6 nominations."
[241] "Nominated for 1 Oscar. Another 6 wins & 1 nomination."
[242] "20 wins & 25 nominations."
[243] "Won 2 Oscars. Another 7 wins & 37 nominations."
[244] "3 wins & 8 nominations."
[245] "8 wins & 11 nominations."
[246] "8 wins & 12 nominations."
[247] "Nominated for 2 Oscars. Another 7 wins & 3 nominations."
[248] "24 wins & 2 nominations."
[249] "Nominated for 1 Oscar. Another 3 wins & 1 nomination."
[250] "17 wins."
[251] "18 wins & 2 nominations."
[252] "Won 1 Oscar. Another 46 wins & 59 nominations."
[253] "Nominated for 1 Golden Globe. Another 5 wins & 9 nominations."
[254] "2 wins & 15 nominations."
[255] "3 wins & 26 nominations."
[256] "6 wins & 13 nominations."
[257] "9 wins & 22 nominations."
[258] "Nominated for 3 Oscars. Another 17 wins & 41 nominations."
[259] "10 wins & 19 nominations."
[260] "10 wins & 9 nominations."
[261] "Nominated for 1 Golden Globe. Another 32 wins & 17 nominations."
[262] "Won 1 Oscar. Another 19 wins."
[263] "5 wins & 7 nominations."
[264] "Nominated for 1 Golden Globe. Another 2 wins & 3 nominations."
[265] "Nominated for 3 Oscars. Another 12 wins & 37 nominations."
[266] "Nominated for 2 Oscars. Another 11 wins & 39 nominations."
[267] "20 wins & 12 nominations."
[268] "4 wins & 9 nominations."
[269] "Won 1 BAFTA Film Award. Another 4 wins & 1 nomination."
[270] "32 wins & 39 nominations."
[271] "Nominated for 1 Golden Globe. Another 25 wins & 84 nominations."
[272] "25 wins & 30 nominations."
[273] "Won 1 Oscar. Another 93 wins & 118 nominations."
[274] "Nominated for 2 Golden Globes. Another 10 wins & 26 nominations."
[275] "31 wins & 11 nominations."
[276] "14 nominations."
[277] "10 wins & 5 nominations."
[278] "Won 2 Oscars. Another 21 wins & 80 nominations."
[279] "5 wins & 13 nominations."
[280] "Nominated for 1 Golden Globe. Another 5 nominations."
[281] "Won 1 Oscar. Another 26 wins & 23 nominations."
[282] "Nominated for 1 Oscar. Another 3 wins."
[283] "16 wins & 15 nominations."
[284] "Won 2 Oscars. Another 53 wins & 131 nominations."
[285] "Nominated for 1 Oscar. Another 3 wins & 3 nominations."
[286] "12 wins & 12 nominations."
[287] "9 wins & 5 nominations."
[288] "26 wins & 16 nominations."
[289] "Won 1 Golden Globe. Another 14 wins & 11 nominations."
[290] "11 wins & 19 nominations."
[291] "7 wins & 3 nominations."
[292] "7 wins & 6 nominations."
[293] "17 wins & 1 nomination."
[294] "Nominated for 1 Oscar. Another 15 wins & 21 nominations."
[295] "Nominated for 5 Oscars. Another 31 wins & 88 nominations."
[296] "12 wins & 10 nominations."
[297] "Nominated for 1 BAFTA Film Award. Another 2 wins & 1 nomination."
[298] "13 wins & 3 nominations."
[299] "8 wins & 3 nominations."
[300] "Nominated for 1 Oscar. Another 4 wins & 16 nominations."
[301] "11 wins & 5 nominations."
[302] "Nominated for 7 Oscars. Another 6 wins & 36 nominations."
[303] "Nominated for 2 Oscars. Another 7 wins & 11 nominations."
[304] "Nominated for 1 Golden Globe. Another 11 wins & 27 nominations."
[305] "Nominated for 3 Oscars. Another 38 wins & 66 nominations."
[306] "Nominated for 1 Oscar. Another 7 wins & 12 nominations."
[307] "14 wins & 6 nominations."
[308] "Won 4 Oscars. Another 6 wins & 11 nominations."
[309] "Nominated for 1 Primetime Emmy. Another 2 nominations."
[310] "Nominated for 3 Oscars. Another 23 wins & 18 nominations."
[311] "2 wins & 18 nominations."
[312] "4 wins & 15 nominations."
[313] "9 wins & 10 nominations."
[314] "5 wins & 24 nominations."
[315] "Nominated for 1 Oscar. Another 14 wins & 45 nominations."
[316] "9 wins & 13 nominations."
[317] "15 wins & 10 nominations."
[318] "Nominated for 1 Oscar. Another 37 wins & 43 nominations."
[319] "Nominated for 1 Oscar. Another 12 wins & 1 nomination."
[320] "Won 1 Oscar. Another 3 wins."
[321] "Nominated for 3 Oscars. Another 13 wins & 25 nominations."
[322] "Nominated for 4 Primetime Emmys. Another 1 win & 3 nominations."
[323] "8 wins & 5 nominations."
[324] "Nominated for 1 Golden Globe. Another 3 wins & 13 nominations."
[325] "24 wins & 3 nominations."
[326] "7 wins & 13 nominations."
[327] "Nominated for 1 Golden Globe. Another 5 wins & 7 nominations."
[328] "Won 1 Oscar. Another 21 wins & 1 nomination."
[329] "7 wins & 16 nominations."
[330] "Nominated for 1 Oscar. Another 4 wins & 10 nominations."
[331] "Nominated for 1 Oscar. Another 13 wins."
[332] "Nominated for 1 Golden Globe. Another 4 wins & 14 nominations."
[333] "9 wins & 27 nominations."
[334] "Won 1 Oscar. Another 12 wins & 21 nominations."
[335] "Nominated for 2 Oscars. Another 24 wins & 67 nominations."
[336] "Nominated for 1 Oscar. Another 11 wins & 28 nominations."
[337] "Won 1 Oscar. Another 60 wins & 95 nominations."
[338] "11 wins & 7 nominations."
[339] "19 wins & 21 nominations."
[340] "3 wins & 11 nominations."
[341] "12 wins & 13 nominations."
[342] "5 wins & 21 nominations."
[343] "1 win & 17 nominations."
[344] "Nominated for 1 Golden Globe. Another 17 wins & 18 nominations."
[345] "1 win & 12 nominations."
[346] "Won 1 Oscar. Another 87 wins & 244 nominations."
[347] "Won 1 Oscar. Another 38 wins & 11 nominations."
[348] "7 wins & 9 nominations."
[349] "2 wins & 11 nominations."
[350] "Won 3 Oscars. Another 4 wins & 8 nominations."
[351] "4 wins & 11 nominations."
[352] "Nominated for 1 Primetime Emmy. Another 2 wins & 1 nomination."
[353] "Won 1 Oscar. Another 1 win & 9 nominations."
[354] "Won 1 Oscar. Another 3 wins & 6 nominations."
[355] "Won 6 Oscars. Another 49 wins & 121 nominations."
[356] "16 wins & 10 nominations."
[357] "7 wins & 11 nominations."
[358] "2 wins & 14 nominations."
[359] "21 wins & 36 nominations."
[360] "Nominated for 1 Oscar. Another 2 wins & 2 nominations."
[361] "Nominated for 1 Oscar. Another 2 wins & 4 nominations."
[362] "Nominated for 1 BAFTA Film Award. Another 44 wins & 7 nominations."
[363] "Nominated for 1 Golden Globe. Another 6 wins & 33 nominations."
[364] "Nominated for 1 Oscar. Another 17 wins & 25 nominations."
[365] "Won 1 Oscar. Another 13 wins & 19 nominations."
[366] "Nominated for 1 Oscar. Another 73 wins & 162 nominations."
[367] "12 nominations."
[368] "20 wins & 14 nominations."
[369] "8 wins & 8 nominations."
[370] "Nominated for 4 Oscars. Another 100 wins & 86 nominations."
[371] "17 wins & 5 nominations."
[372] "Won 2 Oscars. Another 14 wins & 42 nominations."
[373] "Nominated for 10 Oscars. Another 47 wins & 117 nominations."
[374] "Nominated for 3 BAFTA Film Awards. Another 11 wins & 39 nominations."
[375] "9 wins & 8 nominations."
[376] "Nominated for 1 Oscar. Another 5 wins & 28 nominations."
[377] "Nominated for 2 Golden Globes. Another 7 wins & 15 nominations."
[378] "Nominated for 3 Oscars. Another 27 wins & 55 nominations."
[379] "Won 1 BAFTA Film Award. Another 6 wins & 8 nominations."
[380] "Nominated for 3 Oscars. Another 2 wins & 12 nominations."
[381] "Won 1 Primetime Emmy. Another 5 nominations."
[382] "11 wins & 4 nominations."
[383] "Nominated for 1 Oscar. Another 10 wins & 27 nominations."
[384] "Nominated for 1 Oscar. Another 18 wins & 76 nominations."
[385] "10 wins & 16 nominations."
[386] "9 wins & 2 nominations."
[387] "Nominated for 1 Oscar. Another 20 wins & 27 nominations."
[388] "Nominated for 1 Golden Globe. Another 1 win & 4 nominations."
[389] "6 wins & 14 nominations."
[390] "6 wins & 21 nominations."
[391] "Won 1 Oscar. Another 83 wins & 96 nominations."
[392] "Won 2 Oscars. Another 3 wins & 1 nomination."
[393] "Nominated for 1 Golden Globe. Another 14 wins & 34 nominations."
[394] "Nominated for 1 Golden Globe. Another 23 wins & 23 nominations."
[395] "19 wins & 10 nominations."
[396] "Won 6 Oscars. Another 206 wins & 189 nominations."
[397] "Won 1 Oscar. Another 20 wins & 75 nominations."
[398] "10 wins & 14 nominations."
[399] "13 wins & 6 nominations."
[400] "Nominated for 1 BAFTA Film Award. Another 1 win & 1 nomination."
[401] "Nominated for 1 Golden Globe. Another 15 wins & 25 nominations."
[402] "3 wins & 30 nominations."
[403] "9 wins & 3 nominations."
[404] "Nominated for 2 Oscars. Another 14 wins & 56 nominations."
[405] "Nominated for 1 Oscar. Another 8 wins & 19 nominations."
[406] "Nominated for 1 Oscar. Another 8 wins & 1 nomination."
[407] "Nominated for 1 Oscar. Another 16 wins & 53 nominations."
[408] "Nominated for 1 Oscar. Another 3 wins & 13 nominations."
[409] "Nominated for 1 Oscar. Another 6 wins & 20 nominations."
[410] "Nominated for 1 Oscar. Another 32 wins & 32 nominations."
[411] "Nominated for 1 Oscar. Another 2 wins."
[412] "10 wins & 21 nominations."
[413] "10 wins & 3 nominations."
[414] "Won 1 BAFTA Film Award. Another 4 wins."
[415] "Nominated for 2 Oscars. Another 1 win & 2 nominations."
[416] "Nominated for 2 Oscars. Another 10 wins & 25 nominations."
[417] "24 wins & 16 nominations."
[418] "1 win & 14 nominations."
[419] "Nominated for 2 Golden Globes. Another 4 wins & 5 nominations."
[420] "13 wins & 5 nominations."
[421] "Won 1 Oscar. Another 53 wins & 75 nominations."
[422] "Nominated for 1 Oscar. Another 8 wins & 29 nominations."
[423] "Nominated for 1 Oscar. Another 31 wins & 4 nominations."
[424] "11 wins & 15 nominations."
[425] "Nominated for 3 Oscars. Another 6 wins & 10 nominations."
[426] "5 wins & 16 nominations."
[427] "3 wins & 16 nominations."
[428] "Nominated for 1 Oscar. Another 7 wins & 9 nominations."
[429] "Nominated for 4 Oscars. Another 65 wins & 37 nominations."
[430] "6 wins & 33 nominations."
[431] "Nominated for 2 Oscars. Another 1 nomination."
[432] "Won 1 Oscar. Another 31 wins & 73 nominations."
[433] "7 wins & 15 nominations."
[434] "Nominated for 3 Oscars. Another 17 wins & 60 nominations."
[435] "Won 1 Oscar. Another 1 win & 4 nominations."
[436] "Nominated for 1 Golden Globe. Another 28 wins & 32 nominations."
[437] "16 wins & 5 nominations."
[438] "Nominated for 1 Oscar. Another 37 wins & 34 nominations."
[439] "Nominated for 1 BAFTA Film Award. Another 10 wins & 1 nomination."
[440] "Nominated for 1 Oscar. Another 8 wins & 10 nominations."
[441] "Nominated for 5 Oscars. Another 38 wins & 69 nominations."
[442] "Nominated for 1 Oscar. Another 5 nominations."
[443] "22 wins & 43 nominations."
[444] "Nominated for 1 Oscar. Another 1 win & 4 nominations."
[445] "20 wins & 18 nominations."
[446] "Nominated for 1 Oscar. Another 44 wins & 19 nominations."
[447] "36 wins & 22 nominations."
[448] "7 wins & 7 nominations."
[449] "Nominated for 2 Golden Globes. Another 6 wins & 10 nominations."
[450] "Nominated for 1 Golden Globe. Another 2 wins & 8 nominations."
[451] "Nominated for 1 Oscar. Another 6 wins & 6 nominations."
[452] "Nominated for 1 Oscar. Another 6 wins & 5 nominations."
[453] "Nominated for 1 Golden Globe. Another 6 wins & 11 nominations."
[454] "Won 1 Oscar. Another 14 wins & 22 nominations."
[455] "Won 1 Oscar. Another 13 wins & 38 nominations."
[456] "Won 2 Oscars. Another 81 wins & 130 nominations."
[457] "8 wins & 4 nominations."
[458] "11 wins & 6 nominations."
[459] "Won 1 Oscar. Another 27 wins & 20 nominations."
[460] "Won 1 Golden Globe. Another 1 win & 4 nominations."
[461] "4 wins & 20 nominations."
[462] "Nominated for 1 Oscar. Another 11 wins & 21 nominations."
[463] "Nominated for 1 Oscar. Another 2 wins & 1 nomination."
[464] "Won 1 Oscar. Another 10 wins & 50 nominations."
[465] "Nominated for 2 BAFTA Film Awards. Another 10 wins & 25 nominations."
[466] "Nominated for 1 Oscar. Another 13 wins & 3 nominations."
[467] "Nominated for 1 Golden Globe. Another 3 wins & 17 nominations."
[468] "7 wins & 5 nominations."
[469] "Won 1 BAFTA Film Award. Another 1 win."
[470] "Won 1 Oscar. Another 37 wins & 59 nominations."
[471] "10 wins & 6 nominations."
[472] "Nominated for 2 Oscars. Another 2 wins & 3 nominations."
[473] "Nominated for 1 Oscar. Another 9 wins & 39 nominations."
[474] "Nominated for 1 Primetime Emmy. Another 1 win & 5 nominations."
[475] "8 wins & 7 nominations."
[476] "14 wins & 7 nominations."
[477] "Nominated for 1 Oscar. Another 4 wins & 33 nominations."
[478] "5 wins & 20 nominations."
[479] "Nominated for 1 Oscar. Another 6 wins & 3 nominations."
[480] "Won 1 Oscar. Another 19 wins & 46 nominations."
[481] "Nominated for 1 Oscar. Another 1 win & 6 nominations."
[482] "Won 4 Oscars. Another 29 wins & 23 nominations."
[483] "2 wins & 12 nominations."
[484] "Nominated for 1 Golden Globe. Another 1 win & 13 nominations."
[485] "Won 1 Oscar. Another 52 wins & 102 nominations."
[486] "12 wins & 7 nominations."
[487] "Nominated for 1 Oscar. Another 54 wins & 22 nominations."
[488] "Nominated for 1 Oscar. Another 15 wins & 20 nominations."
[489] "Nominated for 1 Oscar. Another 12 wins & 22 nominations."
[490] "Nominated for 1 Golden Globe. Another 11 wins & 13 nominations."
[491] "Nominated for 3 Oscars. Another 54 wins & 63 nominations."
[492] "Won 1 Oscar. Another 3 wins & 1 nomination."
[493] "10 wins & 12 nominations."
[494] "Nominated for 2 Oscars. Another 15 wins & 33 nominations."
[495] "13 wins & 27 nominations."
[496] "Nominated for 1 Golden Globe. Another 23 wins & 26 nominations."
[497] "Nominated for 1 Oscar. Another 4 wins & 2 nominations."
[498] "Nominated for 5 Oscars. Another 7 wins & 30 nominations."
[499] "6 wins & 12 nominations."
[500] "Nominated for 2 Oscars. Another 19 wins & 45 nominations."
[501] "Nominated for 1 Oscar. Another 1 win & 2 nominations."
[502] "Nominated for 1 Oscar. Another 33 wins & 45 nominations."
[503] "Nominated for 1 Primetime Emmy. Another 2 wins & 6 nominations."
[504] "9 wins & 15 nominations."
[505] "Nominated for 1 Oscar. Another 2 nominations."
[506] "Won 1 Oscar. Another 31 wins & 57 nominations."
[507] "Won 1 Oscar. Another 21 wins & 2 nominations."
[508] "4 wins & 18 nominations."
[509] "Nominated for 1 Golden Globe. Another 2 wins & 7 nominations."
[510] "Nominated for 1 Oscar. Another 28 wins & 55 nominations."
[511] "15 wins & 16 nominations."
[512] "Won 5 Oscars. Another 53 wins & 101 nominations."
[513] "Won 3 Oscars. Another 1 win & 13 nominations."
[514] "Nominated for 1 Primetime Emmy. Another 5 nominations."
[515] "Nominated for 1 Golden Globe. Another 3 wins & 21 nominations."
[516] "Won 1 Oscar. Another 17 wins & 35 nominations."
[517] "16 wins."
[518] "Won 1 Oscar. Another 6 wins & 6 nominations."
[519] "Nominated for 1 BAFTA Film Award. Another 3 wins."
[520] "Nominated for 1 Oscar. Another 5 wins & 18 nominations."
[521] "30 wins & 12 nominations."
[522] "Won 1 Oscar. Another 23 wins & 35 nominations."
[523] "Nominated for 1 Oscar. Another 10 wins & 6 nominations."
[524] "12 wins & 15 nominations."
[525] "Nominated for 1 Oscar. Another 7 wins & 14 nominations."
[526] "Nominated for 2 Oscars. Another 54 wins & 55 nominations."
[527] "Nominated for 2 Golden Globes. Another 7 wins & 5 nominations."
[528] "8 wins & 20 nominations."
[529] "10 wins & 18 nominations."
[530] "Won 1 Golden Globe. Another 4 wins & 14 nominations."
[531] "Nominated for 2 Oscars. Another 7 wins & 35 nominations."
[532] "Won 1 Primetime Emmy. Another 3 wins & 2 nominations."
[533] "Won 5 Oscars. Another 30 wins & 13 nominations."
[534] "Won 1 Oscar. Another 2 wins & 9 nominations."
[535] "Nominated for 3 Oscars. Another 18 wins & 41 nominations."
[536] "11 wins & 10 nominations."
[537] "Nominated for 1 Oscar. Another 17 wins & 1 nomination."
[538] "Nominated for 1 Oscar. Another 32 wins & 61 nominations."
[539] "17 wins & 11 nominations."
[540] "Nominated for 1 Golden Globe. Another 4 wins & 8 nominations."
[541] "Nominated for 1 Oscar. Another 21 wins & 20 nominations."
[542] "Nominated for 2 Oscars. Another 14 wins & 23 nominations."
[543] "Nominated for 1 Golden Globe. Another 1 win & 3 nominations."
[544] "Nominated for 1 Oscar. Another 3 wins & 4 nominations."
[545] "Nominated for 1 Oscar. Another 4 wins & 6 nominations."
[546] "Won 2 Oscars. Another 14 wins & 16 nominations."
[547] "Nominated for 1 Primetime Emmy. Another 2 wins."
[548] "Won 4 Oscars. Another 69 wins & 83 nominations."
[549] "Won 1 Oscar. Another 2 wins & 10 nominations."
[550] "Nominated for 1 BAFTA Film Award. Another 2 wins & 3 nominations."
[551] "13 wins & 21 nominations."
[552] "Nominated for 2 Oscars. Another 30 wins & 41 nominations."
[553] "Won 5 Oscars. Another 103 wins & 96 nominations."
[554] "Nominated for 1 Primetime Emmy. Another 4 wins & 2 nominations."
[555] "9 wins & 14 nominations."
[556] "Nominated for 1 Oscar. Another 5 wins & 11 nominations."
[557] "Nominated for 2 Oscars. Another 12 nominations."
[558] "21 wins & 2 nominations."
[559] "Nominated for 1 Oscar. Another 17 wins & 28 nominations."
[560] "Won 1 Oscar. Another 41 wins & 128 nominations."
[561] "Won 1 Golden Globe. Another 1 nomination."
[562] "Nominated for 3 Oscars. Another 48 wins & 75 nominations."
[563] "2 wins & 16 nominations."
[564] "Nominated for 1 Oscar. Another 2 wins & 6 nominations."
[565] "Won 1 Oscar. Another 47 wins & 37 nominations."
[566] "Nominated for 1 Oscar. Another 20 wins & 10 nominations."
[567] "17 wins & 16 nominations."
[568] "Nominated for 4 BAFTA Film Awards. Another 16 wins & 8 nominations."
[569] "Nominated for 1 Oscar. Another 16 wins & 33 nominations."
[570] "Won 1 Oscar. Another 2 wins & 7 nominations."
[571] "Nominated for 1 Golden Globe. Another 7 wins & 24 nominations."
[572] "5 wins & 14 nominations."
[573] "Nominated for 1 Oscar. Another 10 wins & 31 nominations."
[574] "6 wins & 22 nominations."
[575] "7 wins & 14 nominations."
[576] "21 wins & 17 nominations."
[577] "Nominated for 1 Oscar. Another 8 wins."
[578] "Won 1 Oscar. Another 7 wins & 11 nominations."
[579] "10 wins & 10 nominations."
[580] "26 wins & 2 nominations."
[581] "4 wins & 14 nominations."
[582] "Nominated for 1 Oscar. Another 9 wins & 2 nominations."
[583] "10 wins & 7 nominations."
[584] "Nominated for 3 Oscars. Another 28 wins & 51 nominations."
[585] "11 wins & 17 nominations."
[586] "Won 1 Golden Globe. Another 4 wins & 22 nominations."
[587] "21 wins & 19 nominations."
[588] "Nominated for 2 Oscars. Another 5 wins & 9 nominations."
[589] "Nominated for 1 Oscar. Another 4 wins & 4 nominations."
[590] "13 wins & 1 nomination."
[591] "Nominated for 3 Golden Globes. Another 12 wins & 14 nominations."
[592] "Won 1 Oscar. Another 33 wins & 74 nominations."
[593] "Won 1 Oscar. Another 23 wins & 39 nominations."
[594] "Nominated for 1 Oscar. Another 5 wins & 9 nominations."
[595] "41 wins & 16 nominations."
[596] "Nominated for 3 Oscars. Another 20 wins & 60 nominations."
[597] "Nominated for 1 Golden Globe. Another 3 wins & 14 nominations."
[598] "Nominated for 2 Oscars. Another 1 win & 13 nominations."
[599] "15 wins."
[600] "Won 1 Oscar. Another 11 wins & 25 nominations."
[601] "Won 1 BAFTA Film Award. Another 3 wins & 2 nominations."
[602] "8 wins & 19 nominations."
[603] "Nominated for 1 Oscar. Another 3 wins & 17 nominations."
[604] "Won 2 Oscars. Another 11 wins & 27 nominations."
[605] "Nominated for 1 Oscar. Another 20 wins & 28 nominations."
[606] "Nominated for 1 BAFTA Film Award. Another 2 wins & 4 nominations."
[607] "Nominated for 1 Oscar. Another 10 wins & 11 nominations."
[608] "24 wins & 11 nominations."
[609] "15 wins & 12 nominations."
[610] "Won 1 Oscar. Another 10 wins & 3 nominations."
[611] "2 wins & 13 nominations."
[612] "Nominated for 1 Oscar. Another 19 wins & 3 nominations."
[613] "Won 1 BAFTA Film Award. Another 1 win & 1 nomination."
[614] "11 wins & 3 nominations."
[615] "24 wins & 4 nominations."
[616] "Nominated for 1 Oscar. Another 4 wins & 13 nominations."
[617] "Won 1 Oscar. Another 46 wins & 37 nominations."
[618] "Nominated for 1 BAFTA Film Award. Another 6 wins & 13 nominations."
[619] "Nominated for 4 Oscars. Another 14 wins & 35 nominations."
[620] "Nominated for 1 Oscar. Another 1 win & 15 nominations."
[621] "Nominated for 4 Oscars. Another 3 wins & 9 nominations."
[622] "Nominated for 1 Oscar. Another 3 wins & 23 nominations."
[623] "23 wins & 5 nominations."
[624] "15 wins & 19 nominations."
[625] "Won 1 Oscar. Another 3 nominations."
[626] "Nominated for 1 Oscar. Another 1 win & 17 nominations."
[627] "Nominated for 1 Golden Globe. Another 10 wins & 11 nominations."
[628] "Nominated for 5 Oscars. Another 3 wins & 3 nominations."
[629] "Won 1 Oscar. Another 34 wins & 53 nominations."
[630] "Nominated for 3 Oscars. Another 11 wins & 47 nominations."
[631] "6 wins & 15 nominations."
[632] "Nominated for 1 Primetime Emmy. Another 3 wins & 1 nomination."
[633] "Nominated for 1 Oscar. Another 64 wins & 169 nominations."
[634] "Won 1 Oscar. Another 37 wins & 32 nominations."
[635] "5 wins & 28 nominations."
[636] "Nominated for 4 Oscars. Another 15 wins & 30 nominations."
[637] "Nominated for 1 Golden Globe. Another 12 wins & 23 nominations."
[638] "Nominated for 4 Oscars. Another 6 wins & 2 nominations."
[639] "Nominated for 2 Oscars. Another 4 wins & 22 nominations."
[640] "Nominated for 1 Oscar. Another 9 wins & 4 nominations."
[641] "Nominated for 1 Oscar. Another 2 wins & 29 nominations."
[642] "Nominated for 1 BAFTA Film Award. Another 13 wins & 7 nominations."
[643] "14 wins & 3 nominations."
[644] "16 wins & 16 nominations."
[645] "Nominated for 1 Oscar. Another 1 win & 3 nominations."
[646] "Nominated for 1 Oscar. Another 17 wins & 20 nominations."
[647] "13 wins & 10 nominations."
[648] "Nominated for 1 Oscar. Another 7 nominations."
[649] "Nominated for 2 Oscars. Another 13 wins & 12 nominations."
[650] "15 wins & 28 nominations."
[651] "Nominated for 1 Oscar. Another 4 wins & 1 nomination."
[652] "Nominated for 1 Oscar. Another 1 win & 7 nominations."
[653] "17 wins & 17 nominations."
[654] "Nominated for 1 Primetime Emmy. Another 1 nomination."
[655] "15 wins & 27 nominations."
[656] "8 wins & 14 nominations."
[657] "Nominated for 2 Oscars. Another 10 wins & 28 nominations."
[658] "Nominated for 4 Oscars. Another 5 wins & 9 nominations."
[659] "Nominated for 1 Oscar. Another 11 wins & 1 nomination."
[660] "Nominated for 1 Golden Globe. Another 15 wins & 15 nominations."
[661] "Won 1 Oscar. Another 11 wins & 14 nominations."
[662] "Won 5 Oscars. Another 74 wins & 74 nominations."
[663] "Won 7 Oscars. Another 56 wins & 86 nominations."
[664] "11 wins & 8 nominations."
[665] "Nominated for 1 Golden Globe. Another 8 wins & 6 nominations."
[666] "Nominated for 2 Golden Globes. Another 2 wins & 3 nominations."
[667] "Nominated for 1 Oscar. Another 13 wins & 26 nominations."
[668] "Nominated for 2 Golden Globes. Another 17 wins & 15 nominations."
[669] "27 wins & 13 nominations."
[670] "Nominated for 1 Oscar. Another 4 wins & 9 nominations."
[671] "Won 1 Oscar. Another 6 wins & 2 nominations."
[672] "9 wins & 12 nominations."
[673] "Nominated for 1 Golden Globe. Another 1 win & 1 nomination."
[674] "Nominated for 1 Oscar. Another 7 wins & 10 nominations."
[675] "Won 1 Oscar. Another 8 wins & 19 nominations."
[676] "Nominated for 1 Oscar. Another 8 wins & 2 nominations."
[677] "Nominated for 2 Oscars. Another 7 wins & 10 nominations."
[678] "Nominated for 4 Oscars. Another 9 wins & 38 nominations."
[679] "Nominated for 2 Oscars. Another 10 wins & 21 nominations."
[680] "Won 1 Oscar. Another 29 wins & 51 nominations."
[681] "Won 2 Oscars. Another 35 wins & 49 nominations."
[682] "Nominated for 3 Oscars. Another 29 wins & 49 nominations."
[683] "Nominated for 1 Golden Globe. Another 5 wins & 15 nominations."
[684] "Nominated for 2 Oscars. Another 7 wins & 9 nominations."
[685] "Nominated for 1 Oscar. Another 15 wins & 26 nominations."
[686] "16 wins & 3 nominations."
[687] "10 wins & 8 nominations."
[688] "Nominated for 1 Oscar. Another 2 wins & 14 nominations."
[689] "Nominated for 1 BAFTA Film Award. Another 11 wins."
[690] "Nominated for 1 BAFTA Film Award. Another 5 wins & 1 nomination."
[691] "Nominated for 1 Oscar. Another 6 wins & 14 nominations."
[692] "Won 1 Oscar. Another 8 wins & 1 nomination."
[693] "Won 2 Oscars. Another 20 wins & 53 nominations."
[694] "Nominated for 1 Oscar. Another 9 wins & 15 nominations."
[695] "Nominated for 3 Golden Globes. Another 1 nomination."
[696] "Nominated for 1 Oscar. Another 7 wins & 16 nominations."
[697] "14 wins & 11 nominations."
[698] "Nominated for 1 Oscar. Another 7 wins & 21 nominations."
[699] "12 wins & 11 nominations."
[700] "Nominated for 4 Oscars. Another 6 wins & 8 nominations."
[701] "Won 2 Oscars. Another 84 wins & 77 nominations."
[702] "Nominated for 1 Golden Globe. Another 4 wins & 5 nominations."
[703] "5 wins & 11 nominations."
[704] "22 wins & 4 nominations."
[705] "Nominated for 1 Primetime Emmy. Another 5 wins & 2 nominations."
[706] "Nominated for 1 Oscar. Another 2 wins & 7 nominations."
[707] "Nominated for 1 BAFTA Film Award. Another 2 nominations."
[708] "Won 1 Golden Globe. Another 12 wins & 7 nominations."
[709] "Won 4 Oscars. Another 155 wins & 131 nominations."
[710] "Won 1 Oscar. Another 18 wins & 39 nominations."
[711] "Won 1 BAFTA Film Award. Another 8 wins & 8 nominations."
[712] "Nominated for 1 Oscar. Another 10 wins & 7 nominations."
[713] "14 wins & 4 nominations."
[714] "Nominated for 1 Golden Globe. Another 8 wins & 5 nominations."
[715] "Nominated for 1 Golden Globe. Another 3 wins & 7 nominations."
[716] "Nominated for 3 Primetime Emmys. Another 4 nominations."
[717] "Nominated for 1 BAFTA Film Award. Another 5 wins & 2 nominations."
[718] "Won 11 Oscars. Another 110 wins & 73 nominations."
[719] "Nominated for 1 Golden Globe. Another 6 wins & 9 nominations."
[720] "Nominated for 2 Primetime Emmys. Another 2 nominations."
[721] "Nominated for 1 Oscar. Another 8 wins & 15 nominations."
[722] "Nominated for 1 BAFTA Film Award. Another 23 wins & 93 nominations."
[723] "Won 1 Oscar. Another 8 wins & 2 nominations."
[724] "Nominated for 1 Oscar. Another 4 wins & 3 nominations."
[725] "Nominated for 2 Oscars. Another 2 wins & 17 nominations."
[726] "Nominated for 2 Oscars. Another 3 wins & 20 nominations."
[727] "Nominated for 1 Oscar. Another 3 nominations."
[728] "Nominated for 1 Golden Globe. Another 4 wins & 7 nominations."
[729] "Won 1 BAFTA Film Award. Another 2 nominations."
[730] "Won 1 Primetime Emmy. Another 5 wins & 16 nominations."
[731] "Nominated for 1 Golden Globe. Another 10 wins & 18 nominations."
[732] "9 wins & 17 nominations."
[733] "Nominated for 1 BAFTA Film Award. Another 10 wins & 6 nominations."
[734] "Nominated for 1 Oscar. Another 42 wins & 25 nominations."
[735] "Won 1 BAFTA Film Award. Another 8 wins & 3 nominations."
[736] "15 wins & 25 nominations."
[737] "Nominated for 1 Golden Globe. Another 11 wins & 11 nominations."
[738] "Won 1 Oscar. Another 17 wins & 38 nominations."
[739] "12 wins & 3 nominations."
[740] "Won 2 Oscars. Another 73 wins & 52 nominations."
[741] "Nominated for 1 Golden Globe. Another 5 wins & 14 nominations."
[742] "Nominated for 1 Oscar. Another 4 wins & 5 nominations."
[743] "Nominated for 2 Oscars. Another 2 wins & 7 nominations."
[744] "Nominated for 4 Oscars. Another 9 wins & 20 nominations."
[745] "Won 1 Oscar. Another 32 wins & 33 nominations."
[746] "Nominated for 1 Oscar. Another 4 wins & 8 nominations."
[747] "Nominated for 1 Oscar. Another 9 wins & 14 nominations."
[748] "Won 1 Oscar. Another 25 wins & 37 nominations."
[749] "Won 1 Oscar. Another 20 wins & 12 nominations."
[750] "Nominated for 1 Golden Globe. Another 14 wins & 10 nominations."
[751] "Nominated for 1 Oscar. Another 15 wins & 16 nominations."
[752] "Nominated for 1 Oscar. Another 5 wins & 4 nominations."
[753] "Nominated for 1 Golden Globe. Another 7 wins & 5 nominations."
[754] "Nominated for 1 Oscar. Another 4 wins & 12 nominations."
[755] "Nominated for 2 Oscars. Another 4 wins & 15 nominations."
[756] "Won 2 Oscars. Another 3 wins & 11 nominations."
[757] "Nominated for 1 Oscar. Another 10 wins & 10 nominations."
[758] "Nominated for 1 Golden Globe. Another 3 wins & 4 nominations."
[759] "Nominated for 1 Oscar. Another 21 wins & 14 nominations."
[760] "Nominated for 1 Oscar. Another 14 wins & 26 nominations."
[761] "Nominated for 5 Oscars. Another 33 wins & 36 nominations."
[762] "7 wins & 18 nominations."
[763] "Won 1 Oscar. Another 44 wins & 48 nominations."
[764] "Nominated for 1 BAFTA Film Award. Another 6 nominations."
[765] "Won 1 Oscar. Another 13 wins & 11 nominations."
[766] "Nominated for 1 Oscar. Another 3 wins & 5 nominations."
[767] "Nominated for 1 Oscar. Another 20 wins & 26 nominations."
[768] "Nominated for 2 Oscars. Another 10 wins & 13 nominations."
[769] "Nominated for 1 Oscar. Another 3 wins & 2 nominations."
[770] "Won 1 Oscar. Another 9 wins & 4 nominations."
[771] "Won 2 Oscars. Another 24 wins & 49 nominations."
[772] "Won 1 Oscar. Another 18 wins & 23 nominations."
[773] "Nominated for 3 Oscars. Another 9 wins & 21 nominations."
[774] "Won 5 Oscars. Another 26 wins & 29 nominations."
[775] "Nominated for 1 BAFTA Film Award. Another 7 wins & 7 nominations."
[776] "Nominated for 1 Oscar. Another 3 wins & 9 nominations."
[777] "Nominated for 3 Oscars. Another 5 wins & 6 nominations."
[778] "Won 1 Oscar. Another 20 wins & 19 nominations."
[779] "8 wins & 21 nominations."
[780] "Won 1 Golden Globe. Another 5 wins & 15 nominations."
[781] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 6 nominations."
[782] "Won 1 Oscar. Another 30 wins & 27 nominations."
[783] "15 wins & 1 nomination."
[784] "Won 1 Oscar. Another 11 wins & 11 nominations."
[785] "48 wins & 59 nominations."
[786] "Nominated for 1 BAFTA Film Award. Another 6 wins & 14 nominations."
[787] "Nominated for 4 Oscars. Another 10 wins & 13 nominations."
[788] "14 wins & 15 nominations."
[789] "Won 1 Oscar. Another 6 wins & 3 nominations."
[790] "Won 2 Oscars. Another 13 wins & 6 nominations."
[791] "Won 2 Oscars. Another 2 nominations."
[792] "Nominated for 2 Oscars. Another 6 wins & 10 nominations."
[793] "Won 1 Oscar. Another 5 wins & 8 nominations."
[794] "Nominated for 1 Oscar. Another 25 wins & 34 nominations."
[795] "Won 1 Oscar. Another 31 wins & 45 nominations."
[796] "14 wins & 5 nominations."
[797] "Won 1 Golden Globe. Another 6 wins & 12 nominations."
[798] "Nominated for 2 Oscars. Another 9 wins & 18 nominations."
[799] "Nominated for 1 Oscar. Another 5 wins & 8 nominations."
[800] "Nominated for 1 BAFTA Film Award. Another 1 win & 6 nominations."
[801] "Nominated for 1 Oscar. Another 6 wins."
[802] "Nominated for 1 Oscar. Another 19 wins & 2 nominations."
[803] "Won 1 Oscar. Another 3 wins & 5 nominations."
[804] "16 wins & 2 nominations."
[805] "Won 1 Oscar. Another 18 wins & 47 nominations."
[806] "Won 1 Oscar. Another 18 wins & 15 nominations."
[807] "8 wins & 18 nominations."
[808] "Nominated for 2 Oscars. Another 3 wins & 6 nominations."
[809] "Nominated for 1 Golden Globe. Another 2 wins & 10 nominations."
[810] "Nominated for 1 Golden Globe. Another 2 wins & 2 nominations."
[811] "Nominated for 1 Oscar. Another 5 wins & 2 nominations."
[812] "Won 2 Oscars. Another 22 wins & 15 nominations."
[813] "Won 6 Oscars. Another 37 wins & 51 nominations."
[814] "Nominated for 2 Oscars. Another 23 wins & 20 nominations."
[815] "Nominated for 1 Oscar. Another 17 wins & 13 nominations."
[816] "Nominated for 1 Oscar. Another 21 wins & 7 nominations."
[817] "Nominated for 2 Oscars. Another 18 wins & 21 nominations."
[818] "19 wins & 13 nominations."
[819] "Won 1 Oscar. Another 1 win & 8 nominations."
[820] "Nominated for 3 Oscars. Another 5 wins & 14 nominations."
[821] "4 wins & 29 nominations."
[822] "Won 1 Oscar. Another 1 nomination."
[823] "Won 3 Oscars. Another 5 wins & 1 nomination."
[824] "Nominated for 1 Golden Globe. Another 8 wins & 10 nominations."
[825] "Nominated for 1 Oscar. Another 5 wins & 5 nominations."
[826] "Nominated for 2 Oscars. Another 5 wins & 6 nominations."
[827] "21 wins & 9 nominations."
[828] "22 wins & 6 nominations."
[829] "Won 1 Oscar. Another 54 wins & 60 nominations."
[830] "Nominated for 4 Oscars. Another 5 wins & 24 nominations."
[831] "14 wins & 1 nomination."
[832] "Nominated for 2 Oscars. Another 3 wins & 3 nominations."
[833] "Won 1 Oscar. Another 4 wins."
[834] "Nominated for 1 Oscar. Another 7 wins & 15 nominations."
[835] "Won 2 Oscars. Another 17 wins & 22 nominations."
[836] "Nominated for 1 Oscar. Another 4 nominations."
[837] "Won 1 Oscar. Another 2 wins & 3 nominations."
[838] "Won 1 Oscar. Another 3 wins & 10 nominations."
[839] "9 wins & 11 nominations."
[840] "Won 3 Golden Globes. Another 7 wins & 9 nominations."
[841] "Nominated for 2 Golden Globes. Another 1 win & 2 nominations."
[842] "Nominated for 3 Oscars. Another 1 win & 7 nominations."
[843] "Nominated for 1 Oscar. Another 7 wins & 1 nomination."
[844] "Won 1 Oscar. Another 1 win."
[845] "Won 1 BAFTA Film Award. Another 5 wins & 8 nominations."
[846] "Won 1 Golden Globe. Another 2 nominations."
[847] "Nominated for 7 Oscars. Another 7 wins & 21 nominations."
[848] "Nominated for 1 Golden Globe. Another 1 nomination."
[849] "Won 3 Oscars. Another 28 wins & 17 nominations."
[850] "Won 1 Oscar. Another 9 wins & 6 nominations."
[851] "Nominated for 1 Golden Globe. Another 5 wins & 8 nominations."
[852] "Won 2 Oscars. Another 10 wins & 16 nominations."
[853] "18 wins & 5 nominations."
[854] "Won 7 Oscars. Another 71 wins & 33 nominations."
[855] "Nominated for 1 Oscar. Another 17 wins & 16 nominations."
[856] "Nominated for 2 Oscars. Another 4 wins & 12 nominations."
[857] "Nominated for 1 Oscar. Another 5 wins & 3 nominations."
[858] "Nominated for 2 Oscars. Another 3 wins & 11 nominations."
[859] "Won 2 Oscars. Another 25 wins & 18 nominations."
[860] "Nominated for 2 Oscars. Another 5 wins & 18 nominations."
[861] "Nominated for 2 Oscars. Another 2 wins & 15 nominations."
[862] "Won 1 Oscar. Another 24 wins & 14 nominations."
[863] "Nominated for 1 Golden Globe. Another 4 wins & 2 nominations."
[864] "Nominated for 3 Oscars. Another 3 wins & 11 nominations."
[865] "Nominated for 1 Oscar. Another 6 wins & 4 nominations."
[866] "Won 1 Oscar. Another 3 wins & 12 nominations."
[867] "Nominated for 3 Oscars. Another 4 wins & 5 nominations."
[868] "Nominated for 1 Oscar. Another 5 wins & 7 nominations."
[869] "Nominated for 2 Oscars. Another 2 wins & 4 nominations."
[870] "Nominated for 2 Golden Globes. Another 1 nomination."
[871] "Won 3 Oscars. Another 26 wins & 36 nominations."
[872] "Nominated for 2 Oscars. Another 11 wins & 9 nominations."
[873] "Nominated for 2 Oscars. Another 5 nominations."
[874] "Nominated for 2 Oscars. Another 18 wins & 14 nominations."
[875] "Nominated for 1 Oscar. Another 6 nominations."
[876] "Won 1 Oscar. Another 3 wins & 2 nominations."
[877] "Nominated for 2 Oscars. Another 15 wins & 7 nominations."
[878] "Nominated for 2 Oscars. Another 2 wins & 8 nominations."
[879] "Won 1 Oscar. Another 5 wins & 12 nominations."
[880] "Nominated for 1 Oscar. Another 7 wins & 3 nominations."
[881] "Nominated for 1 Golden Globe. Another 19 wins & 11 nominations."
[882] "Nominated for 2 Oscars. Another 10 nominations."
[883] "Nominated for 1 BAFTA Film Award. Another 15 wins & 11 nominations."
[884] "Nominated for 2 Oscars. Another 3 wins & 1 nomination."
[885] "Won 4 Oscars. Another 36 wins & 29 nominations."
[886] "Nominated for 2 Golden Globes. Another 2 nominations."
[887] "Won 1 Oscar. Another 3 wins & 7 nominations."
[888] "Nominated for 2 Golden Globes. Another 2 wins."
[889] "Won 1 Oscar. Another 9 wins & 3 nominations."
[890] "Nominated for 3 Oscars. Another 1 win & 3 nominations."
[891] "Nominated for 3 Oscars. Another 13 wins & 12 nominations."
[892] "Won 2 Oscars. Another 25 wins & 21 nominations."
[893] "Won 2 Oscars. Another 10 wins & 31 nominations."
[894] "Nominated for 2 Oscars. Another 4 wins & 14 nominations."
[895] "Won 1 Oscar. Another 7 wins & 6 nominations."
[896] "Nominated for 1 BAFTA Film Award. Another 13 wins & 11 nominations."
[897] "16 wins & 4 nominations."
[898] "Nominated for 2 Oscars. Another 6 wins & 8 nominations."
[899] "Won 2 Primetime Emmys. Another 5 wins & 5 nominations."
[900] "16 wins & 12 nominations."
[901] "Nominated for 5 Oscars. Another 5 wins & 12 nominations."
[902] "Won 2 Oscars. Another 16 wins & 25 nominations."
[903] "Won 1 Oscar. Another 5 wins."
[904] "Won 1 Oscar. Another 7 wins & 15 nominations."
[905] "14 wins & 8 nominations."
[906] "25 wins & 8 nominations."
[907] "Nominated for 1 BAFTA Film Award. Another 3 wins & 2 nominations."
[908] "Nominated for 2 Oscars. Another 5 wins & 12 nominations."
[909] "Nominated for 1 Oscar. Another 16 wins & 17 nominations."
[910] "Nominated for 2 Golden Globes. Another 6 wins & 54 nominations."
[911] "Nominated for 2 Oscars. Another 1 win & 5 nominations."
[912] "Won 3 Oscars. Another 15 wins & 18 nominations."
[913] "Won 4 Oscars. Another 20 wins & 22 nominations."
[914] "Won 1 Oscar. Another 16 wins & 38 nominations."
[915] "Nominated for 1 Golden Globe. Another 9 wins & 6 nominations."
[916] "Won 1 BAFTA Film Award. Another 15 wins & 7 nominations."
[917] "12 wins & 19 nominations."
[918] "Nominated for 3 Oscars. Another 6 wins & 8 nominations."
[919] "Won 1 Oscar. Another 30 wins & 23 nominations."
[920] "Won 7 Oscars. Another 43 wins & 32 nominations."
[921] "Won 3 Oscars. Another 6 wins & 30 nominations."
[922] "Nominated for 1 Oscar. Another 6 wins & 15 nominations."
[923] "Nominated for 1 Oscar. Another 8 wins & 5 nominations."
[924] "Won 2 Oscars. Another 16 wins & 22 nominations."
[925] "Nominated for 2 Oscars. Another 10 wins & 4 nominations."
[926] "Nominated for 1 Golden Globe. Another 1 win & 5 nominations."
[927] "24 wins & 14 nominations."
[928] "Nominated for 1 BAFTA Film Award. Another 1 win & 3 nominations."
[929] "Nominated for 1 Golden Globe. Another 4 wins."
[930] "Won 1 Oscar. Another 3 wins & 8 nominations."
[931] "Nominated for 1 Oscar. Another 6 wins & 8 nominations."
[932] "Nominated for 2 Oscars. Another 2 wins & 9 nominations."
[933] "Won 1 Oscar. Another 11 wins & 15 nominations."
[934] "14 wins."
[935] "Nominated for 2 Oscars. Another 7 wins & 13 nominations."
[936] "Nominated for 1 Golden Globe. Another 1 win."
[937] "Nominated for 1 Oscar. Another 3 wins & 6 nominations."
[938] "Nominated for 1 Oscar. Another 11 wins & 33 nominations."
[939] "Nominated for 1 Oscar. Another 9 wins & 7 nominations."
[940] "Won 1 Oscar. Another 9 wins & 22 nominations."
[941] "Nominated for 2 Oscars. Another 2 wins & 1 nomination."
[942] "Won 2 Oscars. Another 13 wins & 23 nominations."
[943] "Nominated for 3 Oscars. Another 13 wins & 19 nominations."
[944] "Won 1 Oscar. Another 17 wins & 18 nominations."
[945] "Nominated for 2 Oscars. Another 15 wins & 11 nominations."
[946] "Won 4 Oscars. Another 17 wins & 21 nominations."
[947] "Nominated for 3 Oscars. Another 4 wins & 6 nominations."
[948] "Nominated for 1 Golden Globe. Another 31 wins & 34 nominations."
[949] "Won 3 Oscars. Another 11 wins & 16 nominations."
[950] "Won 1 Oscar. Another 9 wins & 13 nominations."
[951] "Won 1 BAFTA Film Award. Another 1 win & 10 nominations."
[952] "Won 1 Oscar. Another 5 wins & 20 nominations."
[953] "8 wins & 59 nominations."
[954] "Nominated for 1 Oscar. Another 31 wins & 40 nominations."
[955] "32 wins & 85 nominations."
[956] "Won 2 Oscars. Another 20 wins & 19 nominations."
[957] "Nominated for 2 Oscars. Another 4 wins & 10 nominations."
[958] "Nominated for 1 Oscar. Another 14 wins & 20 nominations."
[959] "Nominated for 2 Oscars. Another 4 wins & 7 nominations."
[960] "Nominated for 1 Oscar. Another 4 wins & 15 nominations."
[961] "Won 2 Oscars. Another 32 wins & 62 nominations."
[962] "Nominated for 2 Oscars. Another 11 wins & 12 nominations."
[963] "Won 1 Oscar. Another 8 wins & 9 nominations."
[964] "Nominated for 1 Oscar. Another 7 wins & 4 nominations."
[965] "Nominated for 1 Golden Globe. Another 6 wins & 4 nominations."
[966] "Nominated for 2 Oscars. Another 5 wins & 2 nominations."
[967] "Won 3 Oscars. Another 16 wins & 20 nominations."
[968] "20 wins & 13 nominations."
[969] "Nominated for 3 Golden Globes. Another 19 wins & 39 nominations."
[970] "Won 1 Golden Globe. Another 1 win & 6 nominations."
[971] "Nominated for 2 Golden Globes. Another 2 wins & 1 nomination."
[972] "Won 1 Oscar. Another 16 wins & 24 nominations."
[973] "Won 4 Oscars. Another 22 wins & 21 nominations."
[974] "Won 1 Oscar. Another 1 win & 1 nomination."
[975] "8 wins & 22 nominations."
[976] "Nominated for 1 Oscar. Another 12 wins & 7 nominations."
[977] "Won 1 Golden Globe. Another 2 wins & 15 nominations."
[978] "47 wins & 50 nominations."
[979] "Nominated for 1 Oscar. Another 11 wins & 6 nominations."
[980] "18 wins & 40 nominations."
[981] "Nominated for 3 Oscars. Another 8 wins & 6 nominations."
[982] "Won 3 Oscars. Another 19 wins & 21 nominations."
[983] "Nominated for 2 Oscars. Another 1 win & 9 nominations."
[984] "Nominated for 1 Oscar. Another 5 wins & 49 nominations."
[985] "Won 1 Oscar. Another 8 wins & 16 nominations."
[986] "Nominated for 1 Golden Globe. Another 2 wins & 1 nomination."
[987] "Won 1 BAFTA Film Award. Another 2 wins & 3 nominations."
[988] "Nominated for 2 Oscars. Another 27 wins & 10 nominations."
[989] "Nominated for 2 Golden Globes. Another 3 nominations."
[990] "Nominated for 7 Oscars. Another 13 wins & 16 nominations."
[991] "Nominated for 3 Oscars. Another 5 wins & 11 nominations."
[992] "Nominated for 6 Oscars. Another 12 wins & 10 nominations."
[993] "Nominated for 6 Oscars. Another 7 wins & 16 nominations."
[994] "10 wins & 38 nominations."
[995] "Won 1 Oscar. Another 1 win & 7 nominations."
[996] "Nominated for 5 Oscars. Another 16 wins & 24 nominations."
[997] "Nominated for 1 Golden Globe. Another 6 wins & 3 nominations."
[998] "Won 1 Oscar. Another 4 nominations."
[999] "Nominated for 2 Oscars. Another 2 wins & 2 nominations."
[1000] "Nominated for 1 Golden Globe. Another 2 wins."
[1001] "Nominated for 2 BAFTA Film Awards. Another 1 win."
[1002] "Nominated for 2 Oscars. Another 3 wins & 7 nominations."
[1003] "Nominated for 1 Golden Globe. Another 3 wins & 1 nomination."
[1004] "Nominated for 1 Golden Globe. Another 48 wins & 88 nominations."
[1005] "Nominated for 1 Oscar. Another 2 wins & 3 nominations."
[1006] "16 wins & 1 nomination."
[1007] "10 wins & 23 nominations."
[1008] "Won 1 Oscar. Another 9 wins & 11 nominations."
[1009] "Nominated for 1 Oscar. Another 18 wins & 16 nominations."
[1010] "Nominated for 2 Golden Globes. Another 7 wins & 19 nominations."
[1011] "Won 1 Oscar. Another 4 wins & 12 nominations."
[1012] "Nominated for 3 Oscars. Another 3 wins & 1 nomination."
[1013] "Nominated for 1 Oscar. Another 3 wins & 7 nominations."
[1014] "Nominated for 2 Golden Globes. Another 1 win & 3 nominations."
[1015] "Won 1 Golden Globe. Another 2 wins & 5 nominations."
[1016] "Won 3 Oscars. Another 21 wins & 26 nominations."
[1017] "Won 3 Oscars. Another 12 wins & 17 nominations."
[1018] "Nominated for 1 Golden Globe. Another 11 wins & 16 nominations."
[1019] "Nominated for 1 BAFTA Film Award. Another 3 nominations."
[1020] "Nominated for 2 Oscars. Another 2 wins & 11 nominations."
[1021] "Won 2 Oscars. Another 68 wins & 104 nominations."
[1022] "Nominated for 1 Oscar. Another 13 wins & 13 nominations."
[1023] "Nominated for 3 Oscars. Another 3 wins & 9 nominations."
[1024] "Won 4 Oscars. Another 19 wins & 14 nominations."
[1025] "Won 1 Oscar. Another 2 wins & 1 nomination."
[1026] "Nominated for 1 BAFTA Film Award. Another 4 wins & 1 nomination."
[1027] "Nominated for 3 Golden Globes. Another 2 wins & 3 nominations."
[1028] "Nominated for 4 Oscars. Another 3 wins & 13 nominations."
[1029] "Won 1 Oscar. Another 10 wins & 5 nominations."
[1030] "Won 2 Primetime Emmys. Another 1 nomination."
[1031] "Nominated for 1 Golden Globe. Another 5 wins & 6 nominations."
[1032] "Nominated for 1 Golden Globe. Another 3 wins & 8 nominations."
[1033] "Nominated for 3 Oscars. Another 1 win & 2 nominations."
[1034] "Won 1 Oscar. Another 18 wins & 26 nominations."
[1035] "Nominated for 2 Oscars. Another 8 wins & 3 nominations."
[1036] "Nominated for 2 BAFTA Film Awards. Another 6 wins & 5 nominations."
[1037] "Won 1 Oscar. Another 10 wins & 11 nominations."
[1038] "Nominated for 2 Oscars. Another 4 wins & 5 nominations."
[1039] "Nominated for 1 BAFTA Film Award. Another 2 wins."
[1040] "Nominated for 1 Golden Globe. Another 1 win & 7 nominations."
[1041] "Won 1 Oscar. Another 2 wins & 6 nominations."
[1042] "Nominated for 2 Oscars. Another 2 nominations."
[1043] "Nominated for 1 Oscar. Another 5 wins & 6 nominations."
[1044] "Won 7 Oscars. Another 22 wins & 28 nominations."
[1045] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 3 nominations."
[1046] "Won 1 Oscar. Another 24 wins & 25 nominations."
[1047] "Won 1 Oscar. Another 25 wins & 22 nominations."
[1048] "Won 11 Oscars. Another 174 wins & 113 nominations."
[1049] "Nominated for 3 Oscars. Another 2 wins & 5 nominations."
[1050] "Nominated for 1 BAFTA Film Award. Another 3 wins & 11 nominations."
[1051] "Won 1 Oscar. Another 2 wins & 2 nominations."
[1052] "Won 2 Oscars. Another 10 wins & 26 nominations."
[1053] "Nominated for 2 Golden Globes. Another 1 win & 7 nominations."
[1054] "Won 8 Oscars. Another 33 wins & 14 nominations."
[1055] "Nominated for 3 BAFTA Film Awards. Another 1 win & 1 nomination."
[1056] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 1 nomination."
[1057] "Nominated for 1 Oscar. Another 3 wins & 8 nominations."
[1058] "Nominated for 2 Oscars. Another 1 win & 4 nominations."
[1059] "Nominated for 1 Oscar. Another 11 wins & 47 nominations."
[1060] "Nominated for 3 Oscars. Another 3 wins & 12 nominations."
[1061] "Won 1 Oscar. Another 6 wins & 20 nominations."
[1062] "Won 4 Oscars. Another 16 wins & 14 nominations."
[1063] "Nominated for 2 Golden Globes. Another 11 wins & 7 nominations."
[1064] "Nominated for 1 Golden Globe. Another 15 wins & 8 nominations."
[1065] "Won 2 Oscars. Another 11 wins & 14 nominations."
[1066] "Won 1 Oscar. Another 4 wins & 4 nominations."
[1067] "Nominated for 1 Oscar. Another 8 wins & 3 nominations."
[1068] "Nominated for 1 Oscar. Another 2 wins & 8 nominations."
[1069] "Nominated for 2 Oscars. Another 3 wins & 5 nominations."
[1070] "Nominated for 1 BAFTA Film Award. Another 4 wins & 2 nominations."
[1071] "Nominated for 4 Oscars. Another 2 wins & 3 nominations."
[1072] "Won 1 BAFTA Film Award. Another 6 wins & 6 nominations."
[1073] "Nominated for 1 BAFTA Film Award. Another 3 wins & 1 nomination."
[1074] "Nominated for 3 Oscars. Another 6 wins & 5 nominations."
[1075] "Won 1 Oscar. Another 10 wins & 14 nominations."
[1076] "Won 1 Oscar. Another 4 wins & 1 nomination."
[1077] "Won 1 BAFTA Film Award. Another 1 win & 8 nominations."
[1078] "Won 1 BAFTA Film Award. Another 3 wins & 6 nominations."
[1079] "Won 1 BAFTA Film Award. Another 8 wins & 6 nominations."
[1080] "Won 1 Oscar. Another 19 wins & 1 nomination."
[1081] "Nominated for 2 Oscars. Another 2 wins & 6 nominations."
[1082] "Nominated for 1 Oscar. Another 13 wins & 7 nominations."
[1083] "Nominated for 3 Golden Globes. Another 4 nominations."
[1084] "Nominated for 5 Oscars. Another 2 wins & 13 nominations."
[1085] "Nominated for 4 Oscars. Another 16 wins & 16 nominations."
[1086] "Won 2 Oscars. Another 7 wins & 13 nominations."
[1087] "Won 5 Oscars. Another 26 wins & 17 nominations."
[1088] "Nominated for 1 Oscar. Another 2 wins & 5 nominations."
[1089] "Nominated for 3 Oscars. Another 4 wins & 11 nominations."
[1090] "Nominated for 5 Oscars. Another 43 wins & 106 nominations."
[1091] "Won 1 Oscar. Another 5 wins & 14 nominations."
[1092] "Nominated for 2 Oscars. Another 6 wins & 14 nominations."
[1093] "Nominated for 2 Golden Globes. Another 12 wins & 26 nominations."
[1094] "Nominated for 2 Oscars. Another 11 wins & 16 nominations."
[1095] "6 wins & 19 nominations."
[1096] "Won 1 Golden Globe. Another 1 win & 9 nominations."
[1097] "Won 4 Oscars. Another 46 wins & 32 nominations."
[1098] "Nominated for 1 Golden Globe. Another 4 wins & 3 nominations."
[1099] "Won 8 Oscars. Another 27 wins & 20 nominations."
[1100] "Won 1 Oscar. Another 9 wins & 17 nominations."
[1101] "Won 2 BAFTA Film Awards. Another 1 nomination."
[1102] "Nominated for 3 Oscars. Another 4 wins & 4 nominations."
[1103] "Nominated for 2 Golden Globes. Another 5 nominations."
[1104] "Won 1 Oscar. Another 12 wins & 11 nominations."
[1105] "Won 1 Oscar. Another 24 wins & 29 nominations."
[1106] "Nominated for 1 Golden Globe. Another 6 wins & 1 nomination."
[1107] "Won 1 Oscar. Another 86 wins & 85 nominations."
[1108] "Nominated for 3 Oscars. Another 3 wins & 5 nominations."
[1109] "Won 4 Oscars. Another 14 wins & 19 nominations."
[1110] "Nominated for 1 Oscar. Another 2 wins & 10 nominations."
[1111] "Nominated for 1 BAFTA Film Award. Another 7 wins & 8 nominations."
[1112] "Nominated for 1 Oscar. Another 2 wins & 9 nominations."
[1113] "Nominated for 1 Golden Globe. Another 11 wins & 5 nominations."
[1114] "Won 1 BAFTA Film Award. Another 1 win & 3 nominations."
[1115] "Nominated for 1 Oscar. Another 8 nominations."
[1116] "Won 1 Oscar. Another 12 wins & 4 nominations."
[1117] "Won 3 Oscars. Another 11 wins & 23 nominations."
[1118] "Nominated for 3 Oscars. Another 3 wins & 6 nominations."
[1119] "Nominated for 1 Oscar. Another 4 wins & 14 nominations."
[1120] "Nominated for 1 Golden Globe. Another 4 wins & 4 nominations."
[1121] "Nominated for 8 Oscars. Another 2 wins & 13 nominations."
[1122] "Won 3 Oscars. Another 19 wins & 34 nominations."
[1123] "Nominated for 1 Oscar. Another 13 wins & 6 nominations."
[1124] "Nominated for 1 Golden Globe. Another 2 wins & 6 nominations."
[1125] "Nominated for 5 Oscars. Another 25 wins & 16 nominations."
[1126] "Won 1 Oscar. Another 8 wins & 13 nominations."
[1127] "Nominated for 1 Golden Globe. Another 1 win & 9 nominations."
[1128] "Won 2 Oscars. Another 4 wins & 17 nominations."
[1129] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 9 nominations."
[1130] "Nominated for 2 Oscars. Another 20 wins & 3 nominations."
[1131] "7 wins & 10 nominations."
[1132] "Won 1 Primetime Emmy. Another 1 nomination."
[1133] "Won 2 Oscars. Another 15 wins & 9 nominations."
[1134] "Won 4 Oscars. Another 15 wins & 14 nominations."
[1135] "Won 2 Oscars. Another 22 wins & 24 nominations."
[1136] "Won 1 Oscar. Another 16 wins & 18 nominations."
[1137] "17 wins & 7 nominations."
[1138] "Nominated for 2 Golden Globes. Another 4 nominations."
[1139] "Nominated for 2 Oscars. Another 1 win & 1 nomination."
[1140] "Nominated for 2 Oscars. Another 9 nominations."
[1141] "Nominated for 3 Oscars. Another 2 nominations."
[1142] "Won 1 Oscar. Another 16 wins & 19 nominations."
[1143] "Won 4 Oscars. Another 6 wins & 14 nominations."
[1144] "Won 2 Oscars. Another 18 wins & 32 nominations."
[1145] "Won 1 Primetime Emmy. Another 6 nominations."
[1146] "Won 1 Oscar. Another 12 wins & 15 nominations."
[1147] "Nominated for 2 BAFTA Film Awards. Another 4 wins & 2 nominations."
[1148] "Nominated for 2 Golden Globes. Another 2 wins & 2 nominations."
[1149] "Nominated for 2 Primetime Emmys. Another 5 nominations."
[1150] "Won 5 Oscars. Another 32 wins & 23 nominations."
[1151] "Nominated for 1 Golden Globe. Another 3 wins & 3 nominations."
[1152] "Nominated for 2 Oscars. Another 15 wins & 21 nominations."
[1153] "Won 2 Primetime Emmys. Another 1 win & 6 nominations."
[1154] "Nominated for 1 Oscar. Another 11 wins & 7 nominations."
[1155] "Won 2 Oscars. Another 10 wins & 6 nominations."
[1156] "Nominated for 3 Oscars. Another 3 wins & 16 nominations."
[1157] "Won 3 Oscars. Another 12 wins & 14 nominations."
[1158] "8 wins & 16 nominations."
[1159] "13 wins & 14 nominations."
[1160] "Nominated for 2 Golden Globes. Another 6 wins & 5 nominations."
[1161] "Won 1 Oscar. Another 4 wins & 10 nominations."
[1162] "Won 3 Oscars. Another 11 wins & 14 nominations."
[1163] "Won 1 Oscar. Another 12 wins & 12 nominations."
[1164] "Won 1 Oscar. Another 4 wins & 5 nominations."
[1165] "Nominated for 5 Oscars. Another 9 wins & 11 nominations."
[1166] "Won 3 Oscars. Another 10 wins & 16 nominations."
[1167] "Won 4 Oscars. Another 108 wins & 122 nominations."
[1168] "Nominated for 1 Golden Globe. Another 2 wins & 4 nominations."
[1169] "Won 2 Oscars. Another 14 wins & 14 nominations."
[1170] "Nominated for 3 Golden Globes. Another 3 wins & 7 nominations."
[1171] "Nominated for 4 Oscars. Another 1 win & 3 nominations."
[1172] "Nominated for 3 Oscars. Another 15 wins & 17 nominations."
[1173] "Nominated for 1 BAFTA Film Award. Another 3 wins & 4 nominations."
[1174] "Won 4 Oscars. Another 26 wins & 8 nominations."
[1175] "Won 1 Oscar. Another 13 wins & 37 nominations."
[1176] "Nominated for 3 Oscars. Another 5 wins & 4 nominations."
[1177] "Won 3 Oscars. Another 18 wins & 24 nominations."
[1178] "Nominated for 2 Oscars. Another 1 win & 3 nominations."
[1179] "Nominated for 4 Golden Globes. Another 2 wins & 2 nominations."
[1180] "Nominated for 1 BAFTA Film Award. Another 8 wins."
[1181] "Nominated for 2 Oscars. Another 4 nominations."
[1182] "Nominated for 1 Oscar. Another 4 wins & 11 nominations."
[1183] "Nominated for 3 BAFTA Film Awards. Another 1 nomination."
[1184] "Won 4 Oscars. Another 12 wins & 21 nominations."
[1185] "Won 2 Oscars. Another 4 wins & 11 nominations."
[1186] "Won 7 Oscars. Another 215 wins & 169 nominations."
[1187] "Won 1 Oscar. Another 8 wins."
[1188] "Won 3 Oscars. Another 42 wins & 92 nominations."
[1189] "Nominated for 2 Oscars. Another 7 wins & 2 nominations."
[1190] "Won 4 Oscars. Another 15 wins & 25 nominations."
[1191] "Won 3 Oscars. Another 16 wins & 21 nominations."
[1192] "Nominated for 4 Golden Globes. Another 1 win & 1 nomination."
[1193] "Nominated for 4 Oscars. Another 21 wins & 15 nominations."
[1194] "Nominated for 3 Oscars. Another 1 win & 6 nominations."
[1195] "Won 4 Oscars. Another 13 wins & 13 nominations."
[1196] "Nominated for 2 Oscars. Another 1 win."
[1197] "Won 1 Oscar. Another 7 wins."
[1198] "Nominated for 5 Oscars. Another 6 nominations."
[1199] "Won 2 Oscars. Another 43 wins & 74 nominations."
[1200] "Nominated for 1 Oscar. Another 6 wins & 2 nominations."
[1201] "Nominated for 1 Oscar. Another 39 wins & 47 nominations."
[1202] "Won 1 Oscar. Another 22 wins & 25 nominations."
[1203] "Won 1 BAFTA Film Award. Another 3 wins & 11 nominations."
[1204] "Nominated for 1 Oscar. Another 17 wins & 14 nominations."
[1205] "Won 1 Oscar. Another 5 wins & 11 nominations."
[1206] "Nominated for 3 Oscars. Another 3 wins & 2 nominations."
[1207] "Won 1 Oscar. Another 20 wins & 23 nominations."
[1208] "Won 1 Oscar. Another 3 wins & 3 nominations."
[1209] "45 wins & 31 nominations."
[1210] "Nominated for 6 Oscars. Another 7 wins & 10 nominations."
[1211] "Won 1 BAFTA Film Award. Another 2 wins & 1 nomination."
[1212] "Won 1 Oscar. Another 21 wins & 29 nominations."
[1213] "Nominated for 2 Oscars. Another 10 wins & 5 nominations."
[1214] "Won 1 Primetime Emmy. Another 1 win & 3 nominations."
[1215] "Won 1 Oscar. Another 17 wins & 8 nominations."
[1216] "Nominated for 5 Oscars. Another 9 wins & 8 nominations."
[1217] "12 wins & 26 nominations."
[1218] "Won 1 BAFTA Film Award. Another 1 nomination."
[1219] "Nominated for 3 Primetime Emmys. Another 1 nomination."
[1220] "Won 1 BAFTA Film Award. Another 8 nominations."
[1221] "Nominated for 1 Oscar. Another 3 wins & 11 nominations."
[1222] "Won 2 Oscars. Another 5 nominations."
[1223] "Won 1 Oscar. Another 7 wins & 10 nominations."
[1224] "Won 1 Oscar. Another 1 win & 5 nominations."
[1225] "Won 1 Golden Globe. Another 6 nominations."
[1226] "Won 8 Oscars. Another 27 wins & 15 nominations."
[1227] "Nominated for 3 Oscars. Another 2 wins & 10 nominations."
[1228] "Nominated for 4 Golden Globes. Another 2 wins & 3 nominations."
[1229] "Nominated for 2 BAFTA Film Awards. Another 1 nomination."
[1230] "Nominated for 5 Oscars. Another 5 wins & 3 nominations."
[1231] "Nominated for 4 Oscars. Another 5 wins & 7 nominations."
[1232] "Nominated for 4 Oscars. Another 4 wins & 8 nominations."
[1233] "Won 1 Oscar. Another 10 nominations."
[1234] "Nominated for 3 Oscars. Another 3 wins & 7 nominations."
[1235] "Won 3 Oscars. Another 6 wins & 13 nominations."
[1236] "Nominated for 1 Golden Globe. Another 3 wins & 2 nominations."
[1237] "Won 1 Oscar. Another 8 wins & 5 nominations."
[1238] "Nominated for 4 Oscars. Another 3 wins & 4 nominations."
[1239] "Nominated for 5 Oscars. Another 2 wins & 5 nominations."
[1240] "Won 2 Oscars. Another 1 win & 11 nominations."
[1241] "Won 1 Oscar. Another 3 wins & 11 nominations."
[1242] "Nominated for 4 Oscars. Another 12 wins & 7 nominations."
[1243] "Nominated for 6 BAFTA Film Awards. Another 2 wins & 2 nominations."
[1244] "14 wins & 14 nominations."
[1245] "13 wins & 11 nominations."
[1246] "Won 1 Oscar. Another 3 wins & 19 nominations."
[1247] "Nominated for 1 BAFTA Film Award. Another 4 nominations."
[1248] "Nominated for 3 Oscars. Another 1 win & 4 nominations."
[1249] "Nominated for 4 Oscars. Another 10 wins & 18 nominations."
[1250] "Nominated for 3 Oscars. Another 2 wins & 4 nominations."
[1251] "Won 1 Oscar. Another 6 wins & 7 nominations."
[1252] "Won 1 Oscar. Another 9 wins & 16 nominations."
[1253] "Won 1 Oscar. Another 8 nominations."
[1254] "Won 1 Oscar. Another 14 wins & 25 nominations."
[1255] "23 wins & 37 nominations."
[1256] "Won 7 Oscars. Another 17 wins & 8 nominations."
[1257] "Won 2 Oscars. Another 7 wins & 21 nominations."
[1258] "Nominated for 4 Oscars. Another 1 win & 6 nominations."
[1259] "Won 1 Oscar. Another 1 win & 2 nominations."
[1260] "Won 1 Oscar. Another 5 wins & 16 nominations."
[1261] "Won 1 BAFTA Film Award. Another 5 nominations."
[1262] "Nominated for 2 Oscars. Another 8 wins & 12 nominations."
[1263] "Nominated for 3 Golden Globes. Another 1 win & 2 nominations."
[1264] "Won 2 BAFTA Film Awards. Another 3 wins & 4 nominations."
[1265] "Won 1 Golden Globe. Another 4 nominations."
[1266] "Won 3 Oscars. Another 24 wins & 15 nominations."
[1267] "Won 1 Golden Globe. Another 7 wins & 8 nominations."
[1268] "Nominated for 3 Oscars. Another 3 nominations."
[1269] "Won 2 BAFTA Film Awards. Another 1 win & 1 nomination."
[1270] "Won 1 Oscar. Another 9 wins & 23 nominations."
[1271] "Nominated for 10 Oscars. Another 37 wins & 148 nominations."
[1272] "Won 1 Oscar. Another 5 wins & 18 nominations."
[1273] "Won 1 Oscar. Another 13 wins & 7 nominations."
[1274] "Won 1 Oscar. Another 6 wins & 9 nominations."
[1275] "Nominated for 3 Golden Globes. Another 1 win & 1 nomination."
[1276] "6 wins & 17 nominations."
[1277] "Nominated for 2 Oscars. Another 1 win & 7 nominations."
[1278] "Won 1 Oscar. Another 6 wins & 16 nominations."
[1279] "Won 5 Oscars. Another 7 wins & 25 nominations."
[1280] "Nominated for 4 Oscars. Another 7 wins & 5 nominations."
[1281] "Won 1 Oscar. Another 9 wins & 12 nominations."
[1282] "Won 1 Oscar. Another 1 win & 6 nominations."
[1283] "Won 6 Oscars. Another 39 wins & 28 nominations."
[1284] "Won 2 Oscars. Another 20 wins & 27 nominations."
[1285] "Won 1 BAFTA Film Award. Another 25 wins & 39 nominations."
[1286] "Won 2 Oscars. Another 7 wins & 22 nominations."
[1287] "Nominated for 4 Oscars. Another 4 wins & 5 nominations."
[1288] "Won 5 Oscars. Another 17 wins & 14 nominations."
[1289] "Won 1 Oscar. Another 4 wins & 11 nominations."
[1290] "Nominated for 1 BAFTA Film Award. Another 1 win & 2 nominations."
[1291] "Won 1 BAFTA Film Award. Another 1 win & 4 nominations."
[1292] "Won 2 Oscars. Another 3 wins & 7 nominations."
[1293] "Won 2 Oscars. Another 2 wins & 6 nominations."
[1294] "Nominated for 4 Oscars. Another 5 wins & 13 nominations."
[1295] "Won 3 Oscars. Another 4 nominations."
[1296] "Won 2 Oscars. Another 14 wins & 15 nominations."
[1297] "Won 2 Oscars. Another 4 wins & 5 nominations."
[1298] "Nominated for 3 Oscars. Another 102 wins & 111 nominations."
[1299] "Won 5 Oscars. Another 17 wins & 23 nominations."
[1300] "Won 1 Oscar. Another 9 wins & 14 nominations."
[1301] "Won 3 Oscars. Another 15 wins & 8 nominations."
[1302] "Won 5 Oscars. Another 16 wins & 13 nominations."
[1303] "Nominated for 2 BAFTA Film Awards. Another 3 nominations."
[1304] "Nominated for 3 Oscars. Another 2 wins & 1 nomination."
[1305] "Won 2 Oscars. Another 3 wins & 13 nominations."
[1306] "Won 1 Oscar. Another 13 wins & 23 nominations."
[1307] "Nominated for 4 Oscars. Another 13 wins & 6 nominations."
[1308] "Won 1 Golden Globe. Another 2 wins & 3 nominations."
[1309] "Won 1 Golden Globe. Another 1 win & 2 nominations."
[1310] "Won 1 Golden Globe. Another 1 win."
[1311] "Nominated for 4 BAFTA Film Awards. Another 2 wins & 2 nominations."
[1312] "Won 5 Oscars. Another 15 wins & 14 nominations."
[1313] "Won 8 Oscars. Another 15 wins & 13 nominations."
[1314] "Nominated for 2 Oscars. Another 2 wins."
[1315] "Nominated for 2 Oscars. Another 4 wins & 6 nominations."
[1316] "Won 1 Oscar. Another 3 wins & 4 nominations."
[1317] "Nominated for 2 Oscars. Another 3 nominations."
[1318] "Nominated for 6 BAFTA Film Awards. Another 1 nomination."
[1319] "Nominated for 1 Golden Globe. Another 7 wins & 1 nomination."
[1320] "Nominated for 3 Oscars. Another 7 nominations."
[1321] "Won 4 Oscars. Another 2 wins & 13 nominations."
[1322] "Nominated for 1 Golden Globe. Another 2 wins & 5 nominations."
[1323] "Won 1 Oscar. Another 5 wins & 7 nominations."
[1324] "5 wins & 15 nominations."
[1325] "Won 1 Oscar. Another 10 wins & 13 nominations."
[1326] "Nominated for 5 Oscars. Another 1 win & 6 nominations."
[1327] "Nominated for 2 Oscars. Another 3 wins & 8 nominations."
[1328] "Won 4 Oscars. Another 16 wins & 20 nominations."
[1329] "Nominated for 2 Oscars. Another 5 wins & 5 nominations."
[1330] "Nominated for 3 Oscars. Another 1 win & 10 nominations."
[1331] "Nominated for 5 Golden Globes. Another 1 nomination."
[1332] "Won 3 Oscars. Another 7 wins & 5 nominations."
[1333] "Won 7 Oscars. Another 23 wins & 14 nominations."
[1334] "Won 1 Oscar. Another 14 wins & 44 nominations."
[1335] "Nominated for 4 BAFTA Film Awards. Another 1 win."
[1336] "Nominated for 2 BAFTA Film Awards. Another 1 win & 1 nomination."
[1337] "Nominated for 3 Oscars. Another 6 wins & 2 nominations."
[1338] "Nominated for 1 Oscar. Another 7 wins."
[1339] "Won 1 Oscar. Another 1 win & 11 nominations."
[1340] "Nominated for 1 BAFTA Film Award. Another 2 wins & 2 nominations."
[1341] "Won 2 Oscars. Another 10 wins & 12 nominations."
[1342] "Won 1 Golden Globe. Another 1 win & 1 nomination."
[1343] "Nominated for 3 Oscars. Another 6 wins & 11 nominations."
[1344] "Nominated for 5 Oscars. Another 1 win & 11 nominations."
[1345] "Nominated for 5 Oscars. Another 1 win & 7 nominations."
[1346] "Won 2 Oscars. Another 13 wins & 25 nominations."
[1347] "Won 1 Oscar. Another 44 wins & 43 nominations."
[1348] "Nominated for 3 Primetime Emmys. Another 2 wins & 4 nominations."
[1349] "Nominated for 4 Oscars. Another 4 wins & 6 nominations."
[1350] "Won 10 Oscars. Another 18 wins & 11 nominations."
[1351] "Won 1 Oscar. Another 6 nominations."
[1352] "Won 3 Oscars. Another 2 wins & 4 nominations."
[1353] "Won 3 Oscars. Another 8 wins & 12 nominations."
[1354] "Won 1 Oscar. Another 4 wins & 7 nominations."
[1355] "Won 1 Golden Globe. Another 2 wins."
[1356] "Nominated for 4 Oscars. Another 3 wins & 7 nominations."
[1357] "Nominated for 7 Oscars. Another 2 wins & 5 nominations."
[1358] "Won 3 BAFTA Film Awards. Another 7 wins & 3 nominations."
[1359] "Won 1 Oscar. Another 1 win & 3 nominations."
[1360] "Won 1 Oscar. Another 7 wins & 16 nominations."
[1361] "Nominated for 4 Oscars. Another 3 wins & 3 nominations."
[1362] "Nominated for 7 Oscars. Another 9 wins & 11 nominations."
[1363] "Won 11 Oscars. Another 16 wins & 13 nominations."
[1364] "Nominated for 3 Oscars. Another 1 win & 1 nomination."
[1365] "Nominated for 1 BAFTA Film Award. Another 1 win & 4 nominations."
[1366] "Nominated for 2 Oscars. Another 3 wins & 4 nominations."
[1367] "Nominated for 3 Oscars. Another 8 wins & 4 nominations."
[1368] "Won 2 Oscars. Another 8 wins & 15 nominations."
[1369] "Won 1 BAFTA Film Award. Another 2 wins & 4 nominations."
[1370] "Nominated for 2 Golden Globes. Another 1 win & 1 nomination."
[1371] "Nominated for 1 Oscar. Another 24 wins & 17 nominations."
[1372] "Nominated for 6 Oscars. Another 4 wins & 6 nominations."
[1373] "Nominated for 6 Oscars. Another 2 wins & 10 nominations."
[1374] "Won 9 Oscars. Another 12 wins & 9 nominations."
[1375] "Nominated for 2 Oscars. Another 3 wins & 2 nominations."
[1376] "Won 1 Oscar. Another 5 wins & 15 nominations."
[1377] "Nominated for 3 Golden Globes. Another 3 nominations."
[1378] "Won 1 Golden Globe. Another 2 wins & 2 nominations."
[1379] "Won 3 BAFTA Film Awards. Another 3 nominations."
[1380] "Won 2 Oscars. Another 5 wins & 15 nominations."
[1381] "Nominated for 5 Oscars. Another 3 wins & 5 nominations."
[1382] "11 wins & 13 nominations."
[1383] "Won 1 Golden Globe. Another 4 wins & 1 nomination."
[1384] "Won 1 Oscar. Another 5 nominations."
[1385] "Nominated for 3 Oscars. Another 16 wins & 8 nominations."
[1386] "Nominated for 2 Oscars. Another 3 wins & 30 nominations."
[1387] "Nominated for 2 BAFTA Film Awards. Another 6 wins."
[1388] "Nominated for 2 Golden Globes. Another 1 win & 4 nominations."
[1389] "Nominated for 4 Oscars. Another 2 wins & 6 nominations."
[1390] "Nominated for 3 Golden Globes. Another 3 wins & 1 nomination."
[1391] "Nominated for 6 Oscars. Another 1 win."
[1392] "Nominated for 9 Oscars. Another 2 wins & 8 nominations."
[1393] "Nominated for 4 Oscars. Another 1 win & 4 nominations."
[1394] "Won 4 Oscars. Another 4 wins & 18 nominations."
[1395] "Won 1 Golden Globe. Another 3 wins & 5 nominations."
[1396] "Nominated for 6 Oscars. Another 3 wins & 9 nominations."
[1397] "Won 1 Golden Globe. Another 3 wins & 3 nominations."
[1398] "Won 5 Oscars. Another 8 wins & 5 nominations."
[1399] "Nominated for 6 Oscars. Another 5 wins & 4 nominations."
[1400] "Nominated for 1 Oscar. Another 10 wins & 2 nominations."
[1401] "Won 1 Oscar. Another 6 wins & 15 nominations."
[1402] "Won 1 BAFTA Film Award. Another 4 nominations."
[1403] "Won 2 Oscars. Another 2 wins & 4 nominations."
[1404] "Nominated for 3 Oscars. Another 5 wins & 10 nominations."
[1405] "Nominated for 4 Oscars. Another 2 nominations."
[1406] "Nominated for 3 Oscars. Another 1 nomination."
[1407] "Won 1 Oscar. Another 11 wins & 10 nominations."
[1408] "Won 1 Oscar. Another 2 wins & 5 nominations."
[1409] "Won 3 Oscars. Another 2 wins & 6 nominations."
[1410] "Won 4 Oscars. Another 14 wins & 5 nominations."
[1411] "Won 2 Oscars. Another 1 win & 4 nominations."
[1412] "Won 1 Golden Globe. Another 6 wins & 1 nomination."
[1413] "Nominated for 1 BAFTA Film Award. Another 8 wins & 1 nomination."
[1414] "Nominated for 3 Oscars. Another 2 wins & 3 nominations."
[1415] "Nominated for 3 Oscars. Another 1 win."
[1416] "Won 1 BAFTA Film Award. Another 3 nominations."
[1417] "Nominated for 4 Oscars. Another 3 wins & 5 nominations."
[1418] "Won 8 Oscars. Another 21 wins & 9 nominations."
[1419] "Won 2 Oscars. Another 3 nominations."
[1420] "Won 1 Oscar. Another 2 nominations."
[1421] "Won 8 Oscars. Another 14 wins & 7 nominations."
[1422] "Won 1 Oscar. Another 6 wins & 8 nominations."
[1423] "7 wins & 26 nominations."
[1424] "Won 3 Oscars. Another 7 wins & 15 nominations."
[1425] "Nominated for 6 Oscars. Another 4 nominations."
[1426] "Won 4 Oscars. Another 13 wins & 10 nominations."
[1427] "Nominated for 1 BAFTA Film Award. Another 5 wins."
[1428] "Nominated for 3 Oscars. Another 5 nominations."
[1429] "Won 2 Oscars. Another 4 wins & 10 nominations."
[1430] "Nominated for 4 Oscars. Another 1 win & 1 nomination."
[1431] "Nominated for 2 Oscars. Another 5 wins & 7 nominations."
[1432] "30 wins & 2 nominations."
[1433] "Nominated for 5 Oscars. Another 1 win & 3 nominations."
[1434] "Won 1 Golden Globe. Another 6 wins & 11 nominations."
[1435] "Nominated for 1 Primetime Emmy. Another 1 win & 26 nominations."
[1436] "Nominated for 2 Oscars. Another 19 wins & 61 nominations."
[1437] "Nominated for 8 Oscars. Another 4 wins & 3 nominations."
[1438] "Won 6 Oscars. Another 16 wins & 17 nominations."
[1439] "Nominated for 1 Oscar. Another 12 wins & 13 nominations."
[1440] "Won 2 Oscars. Another 3 wins & 5 nominations."
[1441] "Nominated for 7 Oscars. Another 2 nominations."
[1442] "Nominated for 2 Golden Globes. Another 2 wins & 6 nominations."
[1443] "Won 2 Oscars. Another 1 win & 5 nominations."
[1444] "Nominated for 4 Oscars. Another 1 win."
[1445] "Won 2 Oscars. Another 3 wins & 3 nominations."
[1446] "Won 1 Oscar. Another 4 wins & 13 nominations."
[1447] "Won 3 Oscars. Another 87 wins & 131 nominations."
[1448] "9 wins & 6 nominations."
[1449] "24 wins & 5 nominations."
[1450] "22 wins & 17 nominations."
[1451] "Won 3 Oscars. Another 8 wins & 8 nominations."
[1452] "Nominated for 4 Oscars. Another 2 wins."
[1453] "Nominated for 5 Oscars. Another 6 wins & 1 nomination."
[1454] "Nominated for 4 Oscars. Another 2 wins & 4 nominations."
[1455] "Nominated for 1 Oscar. Another 9 wins & 13 nominations."
[1456] "Nominated for 7 Oscars. Another 2 wins & 2 nominations."
[1457] "Won 2 Oscars. Another 1 win & 7 nominations."
[1458] "Won 7 Oscars. Another 10 wins & 4 nominations."
[1459] "Nominated for 3 Oscars. Another 2 wins."
[1460] "Nominated for 4 Oscars. Another 5 wins & 1 nomination."
[1461] "Won 5 Oscars. Another 2 wins & 7 nominations."
[1462] "Nominated for 4 Oscars. Another 1 nomination."
[1463] "Won 1 Oscar. Another 4 wins & 3 nominations."
[1464] "Nominated for 3 Oscars. Another 4 wins & 1 nomination."
[1465] "Nominated for 2 Oscars. Another 5 wins & 1 nomination."
[1466] "Won 6 Oscars. Another 2 wins & 7 nominations."
[1467] "Nominated for 7 Oscars. Another 1 nomination."
[1468] "Nominated for 4 Oscars. Another 2 wins & 2 nominations."
[1469] "Won 1 Oscar. Another 7 wins & 12 nominations."
[1470] "Won 2 Oscars. Another 1 win & 6 nominations."
[1471] "Nominated for 6 Oscars. Another 1 nomination."
[1472] "Won 5 Oscars. Another 5 wins & 6 nominations."
[1473] "9 wins & 18 nominations."
[1474] "Won 2 Oscars. Another 2 wins & 10 nominations."
[1475] "Nominated for 1 Primetime Emmy. Another 3 nominations."
[1476] "Nominated for 6 Oscars. Another 1 win & 1 nomination."
[1477] "Nominated for 6 Oscars. Another 75 wins & 153 nominations."
[1478] "Won 1 Golden Globe. Another 6 wins & 21 nominations."
[1479] "Won 8 Oscars. Another 9 wins & 8 nominations."
[1480] "Nominated for 1 Oscar. Another 3 wins & 28 nominations."
[1481] "Won 2 Oscars. Another 4 nominations."
[1482] "Nominated for 5 Oscars. Another 2 nominations."
[1483] "Won 2 Oscars. Another 3 wins & 4 nominations."
[1484] "Won 1 Oscar. Another 15 wins & 20 nominations."
[1485] "Won 2 Oscars. Another 6 nominations."
[1486] "Nominated for 1 Oscar. Another 11 wins & 4 nominations."
[1487] "Nominated for 4 Oscars. Another 2 wins & 1 nomination."
[1488] "Won 4 Oscars. Another 3 nominations."
[1489] "5 wins & 19 nominations."
[1490] "Won 1 Oscar. Another 4 wins & 6 nominations."
[1491] "Won 1 Oscar. Another 30 wins & 72 nominations."
[1492] "Won 5 Oscars. Another 5 wins & 1 nomination."
[1493] "Won 3 Oscars. Another 1 win & 1 nomination."
[1494] "Won 2 Oscars. Another 1 nomination."
[1495] "Won 2 Oscars. Another 5 wins & 2 nominations."
[1496] "Nominated for 4 Oscars. Another 3 wins."
[1497] "Nominated for 2 Oscars. Another 16 wins & 41 nominations."
[1498] "3 wins & 43 nominations."
[1499] "Nominated for 1 Oscar. Another 7 wins & 42 nominations."
[1500] "4 wins & 28 nominations."
[1501] "Won 1 Golden Globe. Another 3 wins & 1 nomination."
[1502] "Won 2 Oscars. Another 1 win & 2 nominations."
[1503] "Nominated for 2 Oscars. Another 6 wins & 6 nominations."
[1504] "Nominated for 1 Oscar. Another 19 wins & 68 nominations."
[1505] "Won 1 Oscar. Another 32 wins & 101 nominations."
[1506] "Won 3 Oscars. Another 74 wins & 160 nominations."
[1507] "Nominated for 1 Oscar. Another 45 wins & 55 nominations."
[1508] "Nominated for 1 Golden Globe. Another 5 wins & 39 nominations."
[1509] "14 wins & 29 nominations."
[1510] "Won 2 Oscars. Another 112 wins & 124 nominations."
[1511] "Nominated for 1 Golden Globe. Another 1 win & 43 nominations."
[1512] "Won 1 Oscar. Another 7 wins & 26 nominations."
[1513] "Nominated for 3 Oscars. Another 30 wins & 141 nominations."
[1514] "Won 1 Oscar. Another 99 wins & 118 nominations."
[1515] "Won 1 Oscar. Another 28 wins & 88 nominations."
[1516] "Nominated for 7 Oscars. Another 34 wins & 168 nominations."
[1517] "Nominated for 3 Oscars. Another 12 wins & 129 nominations."
[1518] "Nominated for 2 Golden Globes. Another 3 wins & 13 nominations."
[1519] "Nominated for 1 Oscar. Another 24 wins & 39 nominations."
[1520] "15 wins & 31 nominations."
[1521] "Nominated for 2 Golden Globes. Another 5 wins & 21 nominations."
[1522] "Nominated for 1 BAFTA Film Award. Another 2 wins & 27 nominations."
[1523] "Nominated for 1 BAFTA Film Award. Another 18 nominations."
[1524] "5 wins & 52 nominations."
[1525] "1 win & 15 nominations."
[1526] "3 wins & 40 nominations."
[1527] "13 wins & 12 nominations."
[1528] "Won 1 Oscar. Another 61 wins & 133 nominations."
[1529] "Nominated for 1 Golden Globe. Another 20 wins & 25 nominations."
[1530] "20 wins & 29 nominations."
[1531] "Won 1 Oscar. Another 28 wins & 32 nominations."
[1532] "Nominated for 1 Golden Globe. Another 14 wins & 44 nominations."
[1533] "Won 1 Golden Globe. Another 1 win & 17 nominations."
[1534] "Won 1 Oscar. Another 56 wins & 87 nominations."
[1535] "Nominated for 3 Oscars. Another 11 wins & 69 nominations."
[1536] "Won 1 Oscar. Another 18 wins & 38 nominations."
[1537] "Nominated for 4 Oscars. Another 19 wins & 57 nominations."
[1538] "Nominated for 1 Oscar. Another 6 wins & 45 nominations."
[1539] "7 wins & 19 nominations."
[1540] "Nominated for 2 Oscars. Another 14 wins & 90 nominations."
[1541] "Nominated for 2 Oscars. Another 10 wins & 61 nominations."
[1542] "17 wins & 3 nominations."
[1543] "Won 1 Oscar. Another 44 wins & 149 nominations."
[1544] "Nominated for 1 Golden Globe. Another 15 wins & 26 nominations."
[1545] "Nominated for 1 Oscar. Another 4 wins & 21 nominations."
[1546] "Won 1 Oscar. Another 24 wins & 117 nominations."
[1547] "Won 1 Oscar. Another 13 wins & 53 nominations."
[1548] "Won 1 Oscar. Another 39 wins & 132 nominations."
[1549] "Nominated for 1 Oscar. Another 42 wins & 116 nominations."
[1550] "Won 4 Oscars. Another 182 wins & 264 nominations."
[1551] "Nominated for 1 Golden Globe. Another 2 wins & 27 nominations."
[1552] "Nominated for 2 Golden Globes. Another 6 wins & 18 nominations."
[1553] "Nominated for 1 Oscar. Another 8 wins & 58 nominations."
[1554] "Nominated for 2 Oscars. Another 48 wins & 92 nominations."
[1555] "Nominated for 1 Oscar. Another 2 wins & 11 nominations."
[1556] "Won 1 Oscar. Another 169 wins & 202 nominations."
[1557] "Nominated for 1 Oscar. Another 2 wins & 16 nominations."
[1558] "19 wins & 94 nominations."
[1559] "5 wins & 22 nominations."
[1560] "7 wins & 22 nominations."
[1561] "Nominated for 1 Oscar. Another 13 wins & 59 nominations."
[1562] "9 wins & 28 nominations."
[1563] "11 wins & 34 nominations."
[1564] "Nominated for 1 Oscar. Another 9 wins & 40 nominations."
[1565] "Nominated for 1 Oscar. Another 15 wins & 40 nominations."
[1566] "15 wins & 30 nominations."
[1567] "Won 1 Oscar. Another 65 wins & 82 nominations."
[1568] "3 wins & 29 nominations."
[1569] "1 win & 19 nominations."
[1570] "Nominated for 1 Golden Globe. Another 16 nominations."
[1571] "Won 4 Oscars. Another 121 wins & 213 nominations."
[1572] "Nominated for 1 BAFTA Film Award. Another 28 wins & 35 nominations."
[1573] "Nominated for 1 Oscar. Another 69 wins & 59 nominations."
[1574] "14 wins & 25 nominations."
[1575] "Nominated for 2 Oscars. Another 15 wins & 61 nominations."
[1576] "Nominated for 2 Oscars. Another 5 wins & 14 nominations."
[1577] "Nominated for 5 Oscars. Another 35 wins & 154 nominations."
[1578] "Won 1 Oscar. Another 76 wins & 174 nominations."
[1579] "1 win & 23 nominations."
[1580] "Nominated for 1 Oscar. Another 12 wins & 71 nominations."
[1581] "Nominated for 10 Oscars. Another 65 wins & 202 nominations."
[1582] "Nominated for 3 Oscars. Another 14 wins & 80 nominations."
[1583] "Nominated for 2 Oscars. Another 46 wins & 157 nominations."
[1584] "Nominated for 1 Oscar. Another 10 wins & 29 nominations."
[1585] "Nominated for 4 Oscars. Another 31 wins & 75 nominations."
[1586] "Nominated for 1 Golden Globe. Another 21 wins & 59 nominations."
[1587] "Nominated for 6 Oscars. Another 26 wins & 157 nominations."
[1588] "Won 3 Oscars. Another 79 wins & 78 nominations."
[1589] "Nominated for 1 Oscar. Another 3 wins & 45 nominations."
[1590] "Won 3 Oscars. Another 233 wins & 312 nominations."
[1591] "Nominated for 6 Oscars. Another 16 wins & 138 nominations."
[1592] "1 win & 28 nominations."
[1593] "Nominated for 1 Oscar. Another 10 wins & 34 nominations."
[1594] "Nominated for 1 Golden Globe. Another 8 wins & 36 nominations."
[1595] "Nominated for 2 BAFTA Film Awards. Another 17 wins & 50 nominations."
[1596] "10 wins & 30 nominations."
[1597] "Nominated for 1 Oscar. Another 51 wins & 40 nominations."
[1598] "10 wins & 20 nominations."
[1599] "14 wins & 21 nominations."
[1600] "39 wins & 52 nominations."
[1601] "Nominated for 1 BAFTA Film Award. Another 4 wins & 42 nominations."
[1602] "5 wins & 30 nominations."
[1603] "Nominated for 2 Oscars. Another 12 wins & 63 nominations."
[1604] "Nominated for 2 Oscars. Another 4 wins & 17 nominations."
[1605] "3 wins & 23 nominations."
[1606] "Nominated for 1 BAFTA Film Award. Another 9 wins & 54 nominations."
[1607] "Won 1 Oscar. Another 20 wins & 25 nominations."
[1608] "7 wins & 42 nominations."
[1609] "22 wins & 92 nominations."
[1610] "Nominated for 1 Oscar. Another 19 wins & 57 nominations."
[1611] "2 wins & 21 nominations."
[1612] "Nominated for 1 Oscar. Another 6 wins & 46 nominations."
[1613] "Nominated for 1 Oscar. Another 17 wins & 53 nominations."
[1614] "3 wins & 17 nominations."
[1615] "6 wins & 16 nominations."
[1616] "11 wins & 26 nominations."
[1617] "5 wins & 31 nominations."
[1618] "4 wins & 34 nominations."
[1619] "Won 2 Oscars. Another 53 wins & 143 nominations."
[1620] "Nominated for 1 Oscar. Another 27 wins & 65 nominations."
[1621] "Won 1 Oscar. Another 76 wins & 101 nominations."
[1622] "Won 1 Oscar. Another 84 wins & 167 nominations."
[1623] "Nominated for 3 Oscars. Another 9 wins & 70 nominations."
[1624] "Won 4 Oscars. Another 76 wins & 125 nominations."
[1625] "Nominated for 1 Golden Globe. Another 14 wins & 30 nominations."
[1626] "Won 1 Oscar. Another 86 wins & 145 nominations."
[1627] "20 wins & 11 nominations."
[1628] "13 wins & 19 nominations."
[1629] "Won 2 Oscars. Another 108 wins & 242 nominations."
[1630] "Won 2 Oscars. Another 64 wins & 115 nominations."
[1631] "Nominated for 1 Oscar. Another 33 wins & 40 nominations."
[1632] "Nominated for 1 Golden Globe. Another 16 wins & 72 nominations."
[1633] "Nominated for 1 Oscar. Another 18 wins & 62 nominations."
[1634] "28 wins & 68 nominations."
[1635] "3 wins & 21 nominations."
[1636] "Won 3 Oscars. Another 91 wins & 150 nominations."
[1637] "Nominated for 1 Oscar. Another 5 wins & 41 nominations."
[1638] "6 wins & 20 nominations."
[1639] "15 wins & 43 nominations."
[1640] "18 wins & 48 nominations."
[1641] "Nominated for 3 Oscars. Another 76 wins & 179 nominations."
[1642] "7 wins & 33 nominations."
[1643] "Nominated for 1 Oscar. Another 19 wins & 44 nominations."
[1644] "9 wins & 20 nominations."
[1645] "Nominated for 1 BAFTA Film Award. Another 38 wins & 96 nominations."
[1646] "2 wins & 31 nominations."
[1647] "8 wins & 13 nominations."
[1648] "Nominated for 4 Oscars. Another 91 wins & 123 nominations."
[1649] "Nominated for 1 Oscar. Another 6 wins & 43 nominations."
[1650] "1 win & 20 nominations."
[1651] "Nominated for 2 Oscars. Another 12 wins & 31 nominations."
[1652] "Nominated for 1 Oscar. Another 37 wins & 116 nominations."
[1653] "Nominated for 2 Golden Globes. Another 4 wins & 19 nominations."
[1654] "Nominated for 1 Oscar. Another 34 wins & 75 nominations."
[1655] "Nominated for 1 Oscar. Another 19 nominations."
[1656] "18 wins & 33 nominations."
[1657] "Nominated for 1 Golden Globe. Another 33 wins & 42 nominations."
[1658] "11 wins & 18 nominations."
[1659] "Nominated for 1 Golden Globe. Another 1 win & 6 nominations."
[1660] "12 wins & 4 nominations."
[1661] "10 wins & 15 nominations."
[1662] "Won 2 Oscars. Another 23 wins & 46 nominations."
[1663] "Nominated for 6 Oscars. Another 16 wins & 70 nominations."
[1664] "Nominated for 3 Oscars. Another 18 wins & 39 nominations."
[1665] "Nominated for 1 Oscar. Another 22 wins & 59 nominations."
[1666] "Won 1 Oscar. Another 26 wins & 85 nominations."
[1667] "Nominated for 1 Golden Globe. Another 27 wins & 61 nominations."
[1668] "Nominated for 1 Golden Globe. Another 3 wins & 29 nominations."
[1669] "Nominated for 3 Oscars. Another 34 wins & 88 nominations."
[1670] "Nominated for 1 BAFTA Film Award. Another 10 wins & 16 nominations."
[1671] "Won 5 Oscars. Another 146 wins & 185 nominations."
[1672] "Nominated for 2 Oscars. Another 19 wins & 59 nominations."
[1673] "Nominated for 1 Golden Globe. Another 18 wins & 26 nominations."
[1674] "Won 1 Oscar. Another 15 wins & 41 nominations."
[1675] "Won 5 Oscars. Another 53 wins & 175 nominations."
[1676] "11 wins & 21 nominations."
[1677] "Won 1 Oscar. Another 65 wins & 140 nominations."
[1678] "Nominated for 1 Golden Globe. Another 5 wins & 16 nominations."
[1679] "22 wins & 68 nominations."
[1680] "Nominated for 1 Oscar. Another 8 wins & 23 nominations."
[1681] "15 wins & 32 nominations."
[1682] "Nominated for 1 Oscar. Another 9 wins & 34 nominations."
[1683] "41 wins & 35 nominations."
[1684] "Nominated for 6 Oscars. Another 29 wins & 74 nominations."
[1685] "Won 1 Oscar. Another 79 wins & 112 nominations."
[1686] "Nominated for 1 Oscar. Another 20 wins & 41 nominations."
[1687] "Nominated for 1 Golden Globe. Another 14 wins & 25 nominations."
[1688] "Nominated for 1 BAFTA Film Award. Another 17 wins & 35 nominations."
[1689] "Nominated for 1 Golden Globe. Another 3 wins & 20 nominations."
[1690] "2 wins & 25 nominations."
[1691] "Nominated for 3 Oscars. Another 10 wins & 40 nominations."
[1692] "Nominated for 1 Golden Globe. Another 1 win & 16 nominations."
[1693] "11 wins & 62 nominations."
[1694] "Won 1 Oscar. Another 34 wins & 27 nominations."
[1695] "20 wins & 35 nominations."
[1696] "Nominated for 1 Oscar. Another 5 wins & 44 nominations."
[1697] "Won 1 Oscar. Another 22 wins & 96 nominations."
[1698] "2 wins & 30 nominations."
[1699] "Nominated for 2 Oscars. Another 25 wins & 67 nominations."
[1700] "5 wins & 29 nominations."
[1701] "5 wins & 26 nominations."
[1702] "Won 1 Oscar. Another 45 wins & 24 nominations."
[1703] "Nominated for 1 Oscar. Another 7 wins & 50 nominations."
[1704] "Nominated for 1 Oscar. Another 20 wins & 52 nominations."
[1705] "Nominated for 1 Oscar. Another 7 wins & 40 nominations."
[1706] "Nominated for 1 Oscar. Another 18 wins & 17 nominations."
[1707] "Nominated for 1 Oscar. Another 10 wins & 48 nominations."
[1708] "Won 2 Oscars. Another 69 wins & 113 nominations."
[1709] "Nominated for 3 Golden Globes. Another 3 wins & 2 nominations."
[1710] "Won 4 Oscars. Another 100 wins & 190 nominations."
[1711] "Won 1 Golden Globe. Another 5 wins & 17 nominations."
[1712] "Nominated for 3 BAFTA Film Awards. Another 3 wins & 14 nominations."
[1713] "Won 1 BAFTA Film Award. Another 8 wins & 23 nominations."
[1714] "Nominated for 6 Oscars. Another 23 wins & 140 nominations."
[1715] "14 wins & 16 nominations."
[1716] "Nominated for 3 BAFTA Film Awards. Another 6 wins & 18 nominations."
[1717] "14 wins & 28 nominations."
[1718] "Won 3 Oscars. Another 161 wins & 162 nominations."
[1719] "4 wins & 23 nominations."
[1720] "Nominated for 1 Oscar. Another 39 wins & 15 nominations."
[1721] "Nominated for 1 Golden Globe. Another 9 wins & 19 nominations."
[1722] "Nominated for 1 Oscar. Another 9 wins & 44 nominations."
[1723] "Nominated for 1 Oscar. Another 38 wins & 54 nominations."
[1724] "16 wins & 58 nominations."
[1725] "5 wins & 27 nominations."
[1726] "Won 4 Oscars. Another 143 wins & 198 nominations."
[1727] "Nominated for 4 Oscars. Another 29 wins & 116 nominations."
[1728] "Nominated for 1 Golden Globe. Another 3 wins & 38 nominations."
[1729] "23 wins & 33 nominations."
[1730] "Won 2 Oscars. Another 57 wins & 89 nominations."
[1731] "Nominated for 4 Oscars. Another 63 wins & 118 nominations."
[1732] "11 wins & 14 nominations."
[1733] "16 wins & 61 nominations."
[1734] "17 wins & 24 nominations."
[1735] "Nominated for 2 Oscars. Another 25 wins & 58 nominations."
[1736] "Nominated for 1 Oscar. Another 7 wins & 5 nominations."
[1737] "32 wins & 51 nominations."
[1738] "Nominated for 1 Oscar. Another 15 wins & 8 nominations."
[1739] "Nominated for 2 Oscars. Another 18 wins & 45 nominations."
[1740] "20 wins & 27 nominations."
[1741] "Nominated for 2 Oscars. Another 3 wins & 15 nominations."
[1742] "Nominated for 2 Oscars. Another 4 wins & 20 nominations."
[1743] "Nominated for 3 Golden Globes. Another 8 wins & 12 nominations."
[1744] "Won 1 Oscar. Another 12 wins & 16 nominations."
[1745] "Nominated for 4 Oscars. Another 8 wins & 56 nominations."
[1746] "Won 2 Oscars. Another 35 wins & 29 nominations."
[1747] "Nominated for 1 Oscar. Another 34 wins & 51 nominations."
[1748] "Nominated for 2 Oscars. Another 10 wins & 33 nominations."
[1749] "Nominated for 1 BAFTA Film Award. Another 5 wins & 25 nominations."
[1750] "Nominated for 1 BAFTA Film Award. Another 5 wins & 30 nominations."
[1751] "Nominated for 3 Oscars. Another 12 wins & 39 nominations."
[1752] "Won 1 Oscar. Another 7 wins & 29 nominations."
[1753] "13 wins & 43 nominations."
[1754] "17 wins & 23 nominations."
[1755] "Nominated for 2 Oscars. Another 15 wins & 44 nominations."
[1756] "Nominated for 1 Golden Globe. Another 7 wins & 48 nominations."
[1757] "Nominated for 3 Oscars. Another 34 wins & 90 nominations."
[1758] "Nominated for 2 Oscars. Another 17 wins & 70 nominations."
[1759] "9 wins & 26 nominations."
[1760] "Nominated for 2 Golden Globes. Another 1 win & 17 nominations."
[1761] "Nominated for 1 Oscar. Another 15 wins & 49 nominations."
[1762] "Won 1 Oscar. Another 123 wins & 154 nominations."
[1763] "Nominated for 4 Oscars. Another 24 wins & 104 nominations."
[1764] "Nominated for 1 Oscar. Another 24 wins & 46 nominations."
[1765] "Nominated for 1 Oscar. Another 8 wins & 30 nominations."
[1766] "Won 6 Oscars. Another 114 wins & 121 nominations."
[1767] "Nominated for 1 Oscar. Another 13 wins & 25 nominations."
[1768] "Nominated for 1 Golden Globe. Another 7 wins & 18 nominations."
[1769] "Nominated for 1 Oscar. Another 7 wins & 19 nominations."
[1770] "Won 1 BAFTA Film Award. Another 24 wins & 31 nominations."
[1771] "Won 1 Golden Globe. Another 10 wins & 26 nominations."
[1772] "Won 2 Oscars. Another 73 wins & 75 nominations."
[1773] "Won 1 Oscar. Another 22 wins & 83 nominations."
[1774] "12 wins & 20 nominations."
[1775] "Nominated for 1 Oscar. Another 7 wins & 41 nominations."
[1776] "Nominated for 3 Oscars. Another 19 wins & 62 nominations."
[1777] "Nominated for 1 Oscar. Another 43 wins & 56 nominations."
[1778] "Won 3 Oscars. Another 73 wins & 146 nominations."
[1779] "Nominated for 2 Oscars. Another 56 wins & 83 nominations."
[1780] "Nominated for 5 Oscars. Another 26 wins & 88 nominations."
[1781] "Nominated for 1 Golden Globe. Another 20 wins & 17 nominations."
[1782] "Won 1 Oscar. Another 24 wins & 47 nominations."
[1783] "Nominated for 1 Golden Globe. Another 7 wins & 19 nominations."
[1784] "Nominated for 5 Oscars. Another 21 wins & 72 nominations."
[1785] "Won 2 Oscars. Another 57 wins & 137 nominations."
[1786] "Nominated for 1 Oscar. Another 1 win & 28 nominations."
[1787] "Nominated for 2 BAFTA Film Awards. Another 4 wins & 29 nominations."
[1788] "Won 8 Oscars. Another 143 wins & 120 nominations."
[1789] "8 wins & 25 nominations."
[1790] "Nominated for 3 Oscars. Another 13 wins & 47 nominations."
[1791] "12 wins & 16 nominations."
[1792] "Nominated for 1 Oscar. Another 31 wins & 59 nominations."
[1793] "Won 1 Oscar. Another 7 wins & 20 nominations."
[1794] "Nominated for 2 Golden Globes. Another 5 wins & 26 nominations."
[1795] "Won 1 Oscar. Another 31 wins & 51 nominations."
[1796] "Nominated for 1 Oscar. Another 10 wins & 39 nominations."
[1797] "Nominated for 2 Oscars. Another 32 wins & 36 nominations."
[1798] "Won 1 Oscar. Another 44 wins & 11 nominations."
[1799] "Nominated for 2 Golden Globes. Another 15 wins & 24 nominations."
[1800] "Won 2 Oscars. Another 146 wins & 142 nominations."
[1801] "Nominated for 1 Oscar. Another 5 wins & 25 nominations."
[1802] "Nominated for 1 Oscar. Another 13 wins & 8 nominations."
[1803] "Nominated for 1 Oscar. Another 14 wins & 38 nominations."
[1804] "Nominated for 1 BAFTA Film Award. Another 9 wins & 34 nominations."
[1805] "13 nominations."
[1806] "Nominated for 1 Oscar. Another 18 wins & 33 nominations."
[1807] "Nominated for 1 Oscar. Another 22 wins & 51 nominations."
[1808] "5 wins & 23 nominations."
[1809] "Won 2 Oscars. Another 105 wins & 133 nominations."
[1810] "Nominated for 1 Oscar. Another 28 wins & 54 nominations."
[1811] "Nominated for 1 Golden Globe. Another 11 wins & 9 nominations."
[1812] "Nominated for 1 Oscar. Another 5 wins & 22 nominations."
[1813] "Nominated for 1 Oscar. Another 6 wins & 22 nominations."
[1814] "9 wins & 21 nominations."
[1815] "Nominated for 2 Primetime Emmys. Another 4 wins & 7 nominations."
[1816] "Won 1 Oscar. Another 48 wins & 145 nominations."
[1817] "Won 1 Oscar. Another 88 wins & 94 nominations."
[1818] "Won 1 Oscar. Another 4 wins & 32 nominations."
[1819] "Nominated for 2 Oscars. Another 17 wins & 31 nominations."
[1820] "Nominated for 2 Oscars. Another 12 wins & 34 nominations."
[1821] "Nominated for 1 Golden Globe. Another 1 win & 14 nominations."
[1822] "Nominated for 1 Oscar. Another 32 wins & 28 nominations."
[1823] "Nominated for 1 Oscar. Another 7 wins & 32 nominations."
[1824] "Won 1 Oscar. Another 5 wins & 29 nominations."
[1825] "Nominated for 1 BAFTA Film Award. Another 30 wins & 32 nominations."
[1826] "Won 1 Oscar. Another 26 wins & 109 nominations."
[1827] "Nominated for 2 Oscars. Another 23 wins & 99 nominations."
[1828] "Nominated for 2 Oscars. Another 25 wins & 64 nominations."
[1829] "Nominated for 1 Oscar. Another 2 wins & 17 nominations."
[1830] "Nominated for 1 Oscar. Another 28 wins & 69 nominations."
[1831] "11 wins & 24 nominations."
[1832] "Won 3 Oscars. Another 25 wins & 38 nominations."
[1833] "Nominated for 1 Oscar. Another 12 wins & 18 nominations."
[1834] "Nominated for 1 Golden Globe. Another 4 wins & 32 nominations."
[1835] "Nominated for 1 BAFTA Film Award. Another 13 wins & 15 nominations."
[1836] "Nominated for 2 BAFTA Film Awards. Another 14 wins & 38 nominations."
[1837] "Nominated for 3 Oscars. Another 19 wins & 40 nominations."
[1838] "Won 1 Oscar. Another 64 wins & 42 nominations."
[1839] "Nominated for 1 Oscar. Another 14 wins & 14 nominations."
[1840] "Nominated for 1 Golden Globe. Another 4 wins & 17 nominations."
[1841] "13 wins & 15 nominations."
[1842] "Nominated for 1 Oscar. Another 2 wins & 18 nominations."
[1843] "7 wins & 25 nominations."
[1844] "Nominated for 2 Oscars. Another 20 wins & 41 nominations."
[1845] "Nominated for 1 BAFTA Film Award. Another 5 wins & 14 nominations."
[1846] "Nominated for 1 BAFTA Film Award. Another 3 wins & 31 nominations."
[1847] "11 wins & 20 nominations."
[1848] "6 wins & 23 nominations."
[1849] "16 wins & 42 nominations."
[1850] "2 wins & 64 nominations."
[1851] "Nominated for 1 Golden Globe. Another 5 wins & 4 nominations."
[1852] "15 wins & 17 nominations."
[1853] "Nominated for 4 Oscars. Another 14 wins & 65 nominations."
[1854] "Nominated for 3 Oscars. Another 41 wins & 71 nominations."
[1855] "Nominated for 1 Oscar. Another 4 wins & 20 nominations."
[1856] "Won 1 Golden Globe. Another 8 wins & 11 nominations."
[1857] "Nominated for 1 Oscar. Another 11 wins & 24 nominations."
[1858] "Won 2 Oscars. Another 66 wins & 87 nominations."
[1859] "Nominated for 3 Oscars. Another 8 wins & 17 nominations."
[1860] "Nominated for 1 Golden Globe. Another 8 wins & 31 nominations."
[1861] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 12 nominations."
[1862] "Won 1 Oscar. Another 18 wins & 24 nominations."
[1863] "Nominated for 1 Oscar. Another 60 wins & 80 nominations."
[1864] "Nominated for 1 Oscar. Another 20 wins & 29 nominations."
[1865] "Nominated for 1 Golden Globe. Another 3 wins & 6 nominations."
[1866] "Nominated for 2 Oscars. Another 5 wins & 32 nominations."
[1867] "Nominated for 2 Oscars. Another 15 wins & 24 nominations."
[1868] "Nominated for 3 Oscars. Another 18 wins & 54 nominations."
[1869] "Won 4 Oscars. Another 90 wins & 123 nominations."
[1870] "Won 1 Oscar. Another 90 wins & 92 nominations."
[1871] "Won 1 Oscar. Another 47 wins & 30 nominations."
[1872] "Nominated for 1 Golden Globe. Another 10 wins & 10 nominations."
[1873] "14 wins & 13 nominations."
[1874] "Nominated for 1 Oscar. Another 10 wins & 9 nominations."
[1875] "Nominated for 1 Oscar. Another 27 wins & 45 nominations."
[1876] "4 wins & 22 nominations."
[1877] "Nominated for 1 Oscar. Another 4 wins & 23 nominations."
[1878] "Won 1 Oscar. Another 42 wins & 51 nominations."
[1879] "Nominated for 1 Oscar. Another 12 wins & 38 nominations."
[1880] "Nominated for 2 Oscars. Another 27 wins & 28 nominations."
[1881] "7 wins & 37 nominations."
[1882] "Won 2 Oscars. Another 30 wins & 10 nominations."
[1883] "Won 2 Primetime Emmys. Another 8 wins & 18 nominations."
[1884] "Nominated for 1 Golden Globe. Another 5 wins & 21 nominations."
[1885] "Nominated for 2 Oscars. Another 27 wins & 46 nominations."
[1886] "11 wins & 23 nominations."
[1887] "Nominated for 2 Golden Globes. Another 12 wins & 30 nominations."
[1888] "7 wins & 27 nominations."
[1889] "Won 1 Oscar. Another 16 wins & 13 nominations."
[1890] "Nominated for 1 BAFTA Film Award. Another 17 wins & 19 nominations."
[1891] "26 wins & 32 nominations."
[1892] "Nominated for 5 Oscars. Another 12 wins & 62 nominations."
[1893] "Won 3 Oscars. Another 129 wins & 121 nominations."
[1894] "Nominated for 2 Oscars. Another 6 wins & 31 nominations."
[1895] "Won 3 Oscars. Another 26 wins & 42 nominations."
[1896] "1 win & 21 nominations."
[1897] "Won 1 Oscar. Another 13 wins & 28 nominations."
[1898] "Nominated for 1 Oscar. Another 12 wins & 41 nominations."
[1899] "Nominated for 4 Oscars. Another 13 wins & 52 nominations."
[1900] "Nominated for 2 Oscars. Another 6 wins & 17 nominations."
[1901] "Nominated for 6 Oscars. Another 38 wins & 119 nominations."
[1902] "Nominated for 1 Oscar. Another 23 wins & 47 nominations."
[1903] "Won 1 Oscar. Another 58 wins & 84 nominations."
[1904] "Nominated for 2 Oscars. Another 35 wins & 72 nominations."
[1905] "Won 1 Oscar. Another 33 wins & 64 nominations."
[1906] "Nominated for 1 Oscar. Another 21 wins & 25 nominations."
[1907] "Won 1 Oscar. Another 25 wins & 44 nominations."
[1908] "Nominated for 1 Oscar. Another 15 wins & 46 nominations."
[1909] "Nominated for 1 Oscar. Another 17 wins & 12 nominations."
[1910] "Nominated for 3 Oscars. Another 13 wins & 40 nominations."
[1911] "Nominated for 1 Oscar. Another 15 wins & 66 nominations."
[1912] "Nominated for 3 Oscars. Another 15 wins & 40 nominations."
[1913] "Nominated for 1 Golden Globe. Another 21 wins & 40 nominations."
[1914] "Nominated for 1 BAFTA Film Award. Another 2 wins & 6 nominations."
[1915] "Nominated for 3 Oscars. Another 6 wins & 35 nominations."
[1916] "Won 5 Oscars. Another 74 wins & 115 nominations."
[1917] "Nominated for 1 Oscar. Another 19 wins & 60 nominations."
[1918] "Nominated for 2 Oscars. Another 17 wins & 33 nominations."
[1919] "Nominated for 3 Oscars. Another 5 wins & 21 nominations."
[1920] "Won 2 Oscars. Another 59 wins & 53 nominations."
[1921] "Won 2 Oscars. Another 52 wins & 51 nominations."
[1922] "54 wins & 7 nominations."
[1923] "12 wins & 9 nominations."
[1924] "Won 1 BAFTA Film Award. Another 5 wins & 11 nominations."
[1925] "Nominated for 3 Oscars. Another 25 wins & 20 nominations."
[1926] "Nominated for 1 Golden Globe. Another 28 wins & 13 nominations."
[1927] "Nominated for 4 Oscars. Another 19 wins & 54 nominations."
[1928] "Nominated for 1 Oscar. Another 9 wins & 25 nominations."
[1929] "2 wins & 23 nominations."
[1930] "Won 1 BAFTA Film Award. Another 28 wins & 31 nominations."
[1931] "42 wins & 18 nominations."
[1932] "Nominated for 1 Oscar. Another 5 wins & 14 nominations."
[1933] "Nominated for 1 Oscar. Another 14 wins & 24 nominations."
[1934] "Won 1 Oscar. Another 17 wins & 6 nominations."
[1935] "Won 1 Oscar. Another 41 wins & 120 nominations."
[1936] "Won 3 Oscars. Another 52 wins & 70 nominations."
[1937] "Nominated for 1 Oscar. Another 10 nominations."
[1938] "Won 2 Oscars. Another 114 wins & 127 nominations."
[1939] "Nominated for 2 Golden Globes. Another 2 wins & 10 nominations."
[1940] "Won 4 Oscars. Another 33 wins & 63 nominations."
[1941] "Nominated for 1 Oscar. Another 10 wins & 45 nominations."
[1942] "Nominated for 1 Oscar. Another 46 wins & 56 nominations."
[1943] "Nominated for 1 Golden Globe. Another 28 wins & 50 nominations."
[1944] "Nominated for 1 Golden Globe. Another 4 wins & 18 nominations."
[1945] "4 wins & 17 nominations."
[1946] "Nominated for 1 BAFTA Film Award. Another 6 wins & 28 nominations."
[1947] "Nominated for 1 Oscar. Another 8 wins & 27 nominations."
[1948] "Won 4 Oscars. Another 95 wins & 127 nominations."
[1949] "Nominated for 2 Oscars. Another 1 win & 20 nominations."
[1950] "Nominated for 1 Oscar. Another 6 wins & 23 nominations."
[1951] "Nominated for 2 Oscars. Another 4 wins & 24 nominations."
[1952] "Nominated for 3 Oscars. Another 8 wins & 18 nominations."
[1953] "Nominated for 1 Oscar. Another 7 wins & 17 nominations."
[1954] "Nominated for 5 Oscars. Another 10 wins & 75 nominations."
[1955] "Nominated for 2 Oscars. Another 2 wins & 25 nominations."
[1956] "Nominated for 7 Oscars. Another 23 wins & 50 nominations."
[1957] "Nominated for 1 Oscar. Another 14 wins & 37 nominations."
[1958] "Nominated for 6 Oscars. Another 32 wins & 47 nominations."
[1959] "21 wins & 18 nominations."
[1960] "16 wins & 21 nominations."
[1961] "Nominated for 2 Golden Globes. Another 4 wins & 15 nominations."
[1962] "Nominated for 1 Oscar. Another 5 wins & 19 nominations."
[1963] "Won 4 Oscars. Another 33 wins & 43 nominations."
[1964] "Nominated for 2 Oscars. Another 5 wins & 8 nominations."
[1965] "Nominated for 7 Oscars. Another 20 wins & 41 nominations."
[1966] "Won 1 Oscar. Another 9 wins & 27 nominations."
[1967] "Nominated for 2 Oscars. Another 14 wins & 22 nominations."
[1968] "Nominated for 1 Oscar. Another 14 wins & 19 nominations."
[1969] "Nominated for 2 Golden Globes. Another 3 wins & 2 nominations."
[1970] "Nominated for 2 Oscars. Another 8 wins & 16 nominations."
[1971] "Nominated for 1 Oscar. Another 4 wins & 18 nominations."
[1972] "4 wins & 16 nominations."
[1973] "Nominated for 1 Golden Globe. Another 2 wins & 9 nominations."
[1974] "Nominated for 2 Oscars. Another 33 wins & 52 nominations."
[1975] "Nominated for 1 Golden Globe. Another 5 wins & 30 nominations."
[1976] "Won 1 Oscar. Another 35 wins & 31 nominations."
[1977] "Nominated for 1 Oscar. Another 9 wins & 33 nominations."
[1978] "Won 9 Oscars. Another 49 wins & 66 nominations."
[1979] "Nominated for 1 Golden Globe. Another 7 wins & 11 nominations."
[1980] "Won 1 Oscar. Another 8 wins & 11 nominations."
[1981] "Nominated for 1 Oscar. Another 8 wins & 20 nominations."
[1982] "Nominated for 1 Oscar. Another 9 wins & 8 nominations."
[1983] "Nominated for 1 Oscar. Another 1 win & 9 nominations."
[1984] "Won 2 Oscars. Another 32 wins & 16 nominations."
[1985] "Nominated for 7 Oscars. Another 16 wins & 20 nominations."
[1986] "Nominated for 1 Oscar. Another 6 wins & 17 nominations."
[1987] "Won 2 Oscars. Another 29 wins & 27 nominations."
[1988] "Won 3 Oscars. Another 56 wins & 42 nominations."
[1989] "Nominated for 8 Oscars. Another 16 wins & 24 nominations."
[1990] "Won 1 Oscar. Another 13 wins & 26 nominations."
[1991] "Won 1 Oscar. Another 11 wins & 28 nominations."
[1992] "Nominated for 4 Oscars. Another 9 wins & 19 nominations."
[1993] "Won 1 Oscar. Another 20 wins & 36 nominations."
[1994] "Nominated for 2 Oscars. Another 11 wins & 18 nominations."
[1995] "Won 3 Oscars. Another 12 wins & 15 nominations."
[1996] "Won 1 Oscar. Another 6 wins & 13 nominations."
[1997] "Nominated for 2 Golden Globes. Another 6 wins & 6 nominations."
[1998] "Nominated for 3 Oscars. Another 25 wins & 22 nominations."
[1999] "Nominated for 7 Oscars. Another 3 wins & 11 nominations."
[2000] "Won 5 Oscars. Another 48 wins & 34 nominations."
[2001] "Nominated for 7 Oscars. Another 5 wins & 13 nominations."
[2002] "Won 1 Oscar. Another 5 wins & 13 nominations."
[2003] "Won 9 Oscars. Another 43 wins & 15 nominations."
[2004] "Nominated for 1 Primetime Emmy. Another 1 win & 3 nominations."
[2005] "Won 1 Oscar. Another 10 wins & 15 nominations."
[2006] "Won 3 Oscars. Another 21 wins & 23 nominations."
[2007] "Nominated for 11 Oscars. Another 13 wins & 13 nominations."
[2008] "Won 2 Oscars. Another 19 wins & 26 nominations."
[2009] "Nominated for 2 Oscars. Another 1 win & 6 nominations."
[2010] "Won 4 Oscars. Another 7 wins & 15 nominations."
[2011] "Nominated for 5 Oscars. Another 3 wins & 9 nominations."
[2012] "Nominated for 6 Oscars. Another 13 wins & 5 nominations."
[2013] "Nominated for 8 Oscars. Another 10 wins & 14 nominations."
[2014] "Nominated for 2 Oscars. Another 6 nominations."
[2015] "Nominated for 2 Oscars. Another 7 wins & 4 nominations."
[2016] "Nominated for 2 Oscars. Another 3 wins & 9 nominations."
[2017] "Won 5 Oscars. Another 17 wins & 26 nominations."
[2018] "Nominated for 4 Oscars. Another 1 win & 7 nominations."
[2019] "Nominated for 3 Oscars. Another 2 wins & 8 nominations."
[2020] "Won 3 BAFTA Film Awards. Another 4 wins & 4 nominations."
[2021] "Nominated for 3 Golden Globes. Another 2 wins & 2 nominations."
[2022] "Nominated for 3 Golden Globes. Another 2 wins & 1 nomination."
[2023] "Won 3 Oscars. Another 9 wins & 13 nominations."
[2024] "Won 6 Oscars. Another 10 wins & 20 nominations."
[2025] "Nominated for 3 Oscars. Another 14 wins & 13 nominations."
[2026] "Won 7 Oscars. Another 10 wins & 6 nominations."
[2027] "Won 3 Oscars. Another 23 wins & 27 nominations."
[2028] "Won 5 Oscars. Another 17 wins & 10 nominations."
[2029] "Won 2 BAFTA Film Awards. Another 1 win & 2 nominations."
[2030] "Nominated for 2 Oscars. Another 5 wins & 4 nominations."
[2031] "Won 6 Oscars. Another 27 wins & 8 nominations."
[2032] "Won 5 Oscars. Another 12 wins & 13 nominations."
[2033] "Nominated for 5 Oscars. Another 1 win & 1 nomination."
[2034] "Nominated for 2 Oscars. Another 2 wins & 5 nominations."
[2035] "Won 2 Oscars. Another 6 wins & 6 nominations."
[2036] "Won 2 Oscars. Another 11 wins & 20 nominations."
[2037] "Won 5 Oscars. Another 19 wins & 8 nominations."
[2038] "Nominated for 8 Oscars. Another 11 wins & 15 nominations."
[2039] "Won 7 Oscars. Another 23 wins & 7 nominations."
[2040] "Won 4 Oscars. Another 13 wins & 15 nominations."
[2041] "Won 7 Oscars. Another 13 wins & 2 nominations."
[2042] "Won 4 Oscars. Another 12 wins & 3 nominations." print(summary(df6)) Title Awards Gross
Length:39952 Length:39952 Min. :0.000e+00
Class :character Class :character 1st Qu.:5.000e+06
Mode :character Mode :character Median :3.006e+07
Mean :9.057e+07
3rd Qu.:1.008e+08
Max. :2.784e+09
NA's :35442 print(head(df6)) Title Awards Gross
1 39 Pounds of Love 3 wins. NA
2 3:am N/A NA
3 500 Years Later 2 wins. NA
4 5th World 1 win. NA
5 90 N/A NA
6 Abel Raises Cain 5 wins. NAawards = stri_extract_all(df6$Awards, regex="\\d+")
award_labels = stri_extract_all(df6$Awards, regex="win|won|nomin")
print(head(awards))[[1]]
[1] "3"
[[2]]
[1] NA
[[3]]
[1] "2"
[[4]]
[1] "1"
[[5]]
[1] NA
[[6]]
[1] "5"print(head(award_labels))[[1]]
[1] "win"
[[2]]
[1] NA
[[3]]
[1] "win"
[[4]]
[1] "win"
[[5]]
[1] NA
[[6]]
[1] "win"wins_list = c()
nominations_list = c()
for (i in 1:length(awards)){
wins = 0
nominations = 0
for(j in 1:length(awards[[i]])){
if(!is.na(award_labels[[i]][j]) & (award_labels[[i]][j] == 'wins' | award_labels[[i]][j] == 'win' | award_labels[[i]][j] == 'won')){
wins = wins + as.numeric(awards[[i]][j])
} else if (!is.na(award_labels[[i]][j]) & award_labels[[i]][j] == 'nomin'){
nominations = nominations + as.numeric(awards[[i]][j])
}
}
wins_list = c(wins_list, wins)
nominations_list = c(nominations_list, nominations)
}
df$wins = wins_list
df$nominations = nominations_list
print(head(df[c('Awards', 'wins', 'nominations')], 20)) Awards wins nominations
1 3 wins. 3 0
2 N/A 0 0
3 2 wins. 2 0
4 1 win. 1 0
5 N/A 0 0
6 5 wins. 5 0
7 N/A 0 0
8 N/A 0 0
9 N/A 0 0
10 N/A 0 0
11 N/A 0 0
12 N/A 0 0
13 5 wins & 4 nominations. 5 4
14 N/A 0 0
15 1 win & 1 nomination. 1 1
16 1 win. 1 0
17 N/A 0 0
18 2 wins & 1 nomination. 2 1
19 N/A 0 0
20 1 nomination. 0 1Q: How did you construct your conversion mechanism? How many rows had valid/non-zero wins or nominations?
A: To construct the conversion mechanism, I first used stri_extract_all to extract all numerical substrings. I then extracted all instances of “win, won, or nomin”. I then assigned the numerical to however it corresponded to the position of “win, won, or nomin”.
# TODO: Plot Gross revenue against wins and nominations
ggplot(df, aes(wins, Gross)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 35442 rows containing non-finite values (stat_summary).
ggplot(df, aes(nominations, Gross)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 35442 rows containing non-finite values (stat_summary).
Q: How does the gross revenue vary by number of awards won and nominations received?
A: Surprisingly, there does not seem to be much of a relationship between gross revenue and awards won.
7. Movie ratings from IMDb and Rotten Tomatoes
There are several variables that describe ratings, including IMDb ratings (imdbRating represents average user ratings and imdbVotes represents the number of user ratings), and multiple Rotten Tomatoes ratings (represented by several variables pre-fixed by tomato). Read up on such ratings on the web (for example rottentomatoes.com/about and www.imdb.com/help/show_leaf?votestopfaq).
Investigate the pairwise relationships between these different descriptors using graphs.
library(GGally)
# TODO: Illustrate how ratings from IMDb and Rotten Tomatoes are related
cols = c('imdbRating', 'imdbVotes', 'tomatoMeter', 'tomatoImage', 'tomatoRating', 'tomatoReviews',
'tomatoFresh', 'tomatoRotten', 'tomatoConsensus', 'tomatoUserMeter', 'tomatoUserRating',
'tomatoUserReviews')
df7 = df[cols]
print(summary(df7)) imdbRating imdbVotes tomatoMeter tomatoImage
Min. :1.000 Min. : 5 Min. : 0.00 Length:39952
1st Qu.:5.600 1st Qu.: 60 1st Qu.: 35.00 Class :character
Median :6.400 Median : 211 Median : 63.00 Mode :character
Mean :6.234 Mean : 12025 Mean : 58.56
3rd Qu.:7.100 3rd Qu.: 1294 3rd Qu.: 83.00
Max. :9.800 Max. :1684836 Max. :100.00
NA's :1134 NA's :1172 NA's :30539
tomatoRating tomatoReviews tomatoFresh tomatoRotten
Min. :0.000 Min. : 1.00 Min. : 0.00 Min. : 0.00
1st Qu.:4.800 1st Qu.: 11.00 1st Qu.: 5.00 1st Qu.: 3.00
Median :6.000 Median : 28.00 Median : 15.00 Median : 9.00
Mean :5.875 Mean : 57.47 Mean : 34.32 Mean : 23.14
3rd Qu.:7.000 3rd Qu.: 88.00 3rd Qu.: 43.00 3rd Qu.: 30.00
Max. :9.800 Max. :360.00 Max. :343.00 Max. :249.00
NA's :30548 NA's :30494 NA's :30494 NA's :30494
tomatoConsensus tomatoUserMeter tomatoUserRating tomatoUserReviews
Length:39952 Min. : 0.00 Min. :0.000 Min. : 0
Class :character 1st Qu.: 38.00 1st Qu.:2.900 1st Qu.: 46
Mode :character Median : 57.00 Median :3.300 Median : 341
Mean : 55.51 Mean :3.264 Mean : 93049
3rd Qu.: 75.00 3rd Qu.:3.600 3rd Qu.: 4947
Max. :100.00 Max. :5.000 Max. :35794556
NA's :21936 NA's :21852 NA's :15980 print(head(df7)) imdbRating imdbVotes tomatoMeter tomatoImage tomatoRating tomatoReviews
1 6.8 234 NA N/A NA NA
2 8.2 13 NA N/A NA NA
3 7.0 161 NA N/A NA NA
4 6.4 31 NA N/A NA NA
5 8.3 18 NA N/A NA NA
6 7.5 197 NA N/A NA NA
tomatoFresh tomatoRotten tomatoConsensus tomatoUserMeter
1 NA NA N/A NA
2 NA NA N/A NA
3 NA NA N/A 88
4 NA NA N/A NA
5 NA NA N/A NA
6 NA NA N/A 60
tomatoUserRating tomatoUserReviews
1 NA 50
2 NA NA
3 4.2 341
4 NA 8
5 NA NA
6 4.0 67ggpairs(df7, columns=c('imdbRating', 'tomatoMeter', 'tomatoRating'), aes())Warning: Removed 1134 rows containing non-finite values (stat_density).Warning in (function (data, mapping, alignPercent = 0.6, method =
"pearson", : Removed 30541 rows containing missing valuesWarning in (function (data, mapping, alignPercent = 0.6, method =
"pearson", : Removed 30550 rows containing missing valuesWarning: Removed 30541 rows containing missing values (geom_point).Warning: Removed 30539 rows containing non-finite values (stat_density).Warning in (function (data, mapping, alignPercent = 0.6, method =
"pearson", : Removed 30548 rows containing missing valuesWarning: Removed 30550 rows containing missing values (geom_point).Warning: Removed 30548 rows containing missing values (geom_point).Warning: Removed 30548 rows containing non-finite values (stat_density).
Q: Comment on the similarities and differences between the user ratings of IMDb and the critics ratings of Rotten Tomatoes.
A: First off, it’s interesting to note that we have significantly more data of IMDB ratings than Rotten tomatoes. In our data, we have about 1100 movies with missing IMDB ratings. However, we have close to 30,000 movies with missing Rotten Tomatoes ratings.
In the plot above we plot the ggpairs of imdbRating, tomatoMeter, and tomatoRating. Notice that the correlation between tomatoMeter and tomatoRating is very high, at 0.94. This makes sense as it’s from the same source. The correlation between tomatoMeter and imdbRating is a bit lower, at around 0.746 vs 0.794 which is the correlation between tomatoRating and imdbRating.
Looking at the summary of the variables as well, it looks like imdbRating and tomatoRating are rated on similar scales. imdbRating looks like it’s rated on a scale of 1 to 10, while tomatoRating looks like it’s rated on a scale of 0 to 9.8. However, tomatoMeter looks like it’s scaled from 0 to 100.
8. Ratings and awards
These ratings typically reflect the general appeal of the movie to the public or gather opinions from a larger body of critics. Whereas awards are given by professional societies that may evaluate a movie on specific attributes, such as artistic performance, screenplay, sound design, etc.
Study the relationship between ratings and awards using graphs (awards here refers to wins and/or nominations).
# TODO: Show how ratings and awards are related
ggplot(df, aes(imdbRating, wins)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 1134 rows containing non-finite values (stat_summary).
ggplot(df, aes(imdbRating, nominations)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 1134 rows containing non-finite values (stat_summary).
ggplot(df, aes(tomatoRating, wins)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 30548 rows containing non-finite values (stat_summary).
ggplot(df, aes(tomatoRating, nominations)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 30548 rows containing non-finite values (stat_summary).
mod1 = lm(wins ~ imdbRating, data = df)
summary(mod1)
Call:
lm(formula = wins ~ imdbRating, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.938 -0.883 -0.531 0.067 52.695
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.439096 0.054933 -26.20 <2e-16 ***
imdbRating 0.351807 0.008653 40.66 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.047 on 38816 degrees of freedom
(1134 observations deleted due to missingness)
Multiple R-squared: 0.04084, Adjusted R-squared: 0.04082
F-statistic: 1653 on 1 and 38816 DF, p-value: < 2.2e-16mod1 = lm(nominations ~ imdbRating, data = df)
summary(mod1)
Call:
lm(formula = nominations ~ imdbRating, data = df)
Residuals:
Min 1Q Median 3Q Max
-4.496 -1.947 -1.156 0.019 229.822
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.94098 0.16743 -23.54 <2e-16 ***
imdbRating 0.87885 0.02637 33.32 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.238 on 38816 degrees of freedom
(1134 observations deleted due to missingness)
Multiple R-squared: 0.02781, Adjusted R-squared: 0.02778
F-statistic: 1110 on 1 and 38816 DF, p-value: < 2.2e-16mod1 = lm(wins ~ tomatoRating, data = df)
summary(mod1)
Call:
lm(formula = wins ~ tomatoRating, data = df)
Residuals:
Min 1Q Median 3Q Max
-3.348 -1.445 -0.744 0.305 51.353
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.30926 0.12455 -10.51 <2e-16 ***
tomatoRating 0.50076 0.02052 24.41 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.043 on 9402 degrees of freedom
(30548 observations deleted due to missingness)
Multiple R-squared: 0.05958, Adjusted R-squared: 0.05948
F-statistic: 595.7 on 1 and 9402 DF, p-value: < 2.2e-16mod1 = lm(nominations ~ tomatoRating, data = df)
summary(mod1)
Call:
lm(formula = nominations ~ tomatoRating, data = df)
Residuals:
Min 1Q Median 3Q Max
-14.050 -4.886 -1.832 1.584 220.595
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.74148 0.46064 -18.98 <2e-16 ***
tomatoRating 2.34962 0.07588 30.96 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.25 on 9402 degrees of freedom
(30548 observations deleted due to missingness)
Multiple R-squared: 0.09253, Adjusted R-squared: 0.09244
F-statistic: 958.7 on 1 and 9402 DF, p-value: < 2.2e-16Q: How good are these ratings in terms of predicting the success of a movie in winning awards or nominations? Is there a high correlation between two variables?
A: One extremely interesting trend from the graph above is that for both imdbRatings and tomatoRatings generally there is a positive correlation between ratings and both wins and nominations. But very interestingly enough, the relationship is actually not monotonic. In the extremely upper regions of ratings, the number of wins and nominations actually decrease!
I fit a linear regression between wins and nominations against ratings field. Notice that in each regression the coefficient is significant, which means that there is a significant positive relationship between rating and nominations and wins. However, the R^2 is relatively low, so the correlation is relatively low.
9. Expected insights
Come up with two new insights (backed up by data and graphs) that is expected. Here “new” means insights that are not an immediate consequence of one of the above tasks. You may use any of the columns already explored above or a different one in the dataset, such as Title, Actors, etc.
# TODO: Find and illustrate two expected insights
df = cbind(df, mtabulate(strsplit(df$Country, ", ")))
print(summary(df)) Title Year Rated Released
Length:39952 Min. :1888 Length:39952 Min. :1893-05-09
Class :character 1st Qu.:1961 Class :character 1st Qu.:1959-02-04
Mode :character Median :1989 Mode :character Median :1989-10-06
Mean :1981 Mean :1980-12-02
3rd Qu.:2001 3rd Qu.:2001-11-07
Max. :2018 Max. :2018-08-01
NA's :4949
Runtime Director Writer Actors
Min. : 1.00 Length:39952 Length:39952 Length:39952
1st Qu.: 72.00 Class :character Class :character Class :character
Median : 90.00 Mode :character Mode :character Mode :character
Mean : 81.78
3rd Qu.:101.00
Max. :873.00
NA's :751
Plot Language Country
Length:39952 Length:39952 Length:39952
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
Awards Poster Metascore imdbRating
Length:39952 Length:39952 Length:39952 Min. :1.000
Class :character Class :character Class :character 1st Qu.:5.600
Mode :character Mode :character Mode :character Median :6.400
Mean :6.234
3rd Qu.:7.100
Max. :9.800
NA's :1134
imdbVotes imdbID Type tomatoMeter
Min. : 5 Length:39952 Length:39952 Min. : 0.00
1st Qu.: 60 Class :character Class :character 1st Qu.: 35.00
Median : 211 Mode :character Mode :character Median : 63.00
Mean : 12025 Mean : 58.56
3rd Qu.: 1294 3rd Qu.: 83.00
Max. :1684836 Max. :100.00
NA's :1172 NA's :30539
tomatoImage tomatoRating tomatoReviews tomatoFresh
Length:39952 Min. :0.000 Min. : 1.00 Min. : 0.00
Class :character 1st Qu.:4.800 1st Qu.: 11.00 1st Qu.: 5.00
Mode :character Median :6.000 Median : 28.00 Median : 15.00
Mean :5.875 Mean : 57.47 Mean : 34.32
3rd Qu.:7.000 3rd Qu.: 88.00 3rd Qu.: 43.00
Max. :9.800 Max. :360.00 Max. :343.00
NA's :30548 NA's :30494 NA's :30494
tomatoRotten tomatoConsensus tomatoUserMeter tomatoUserRating
Min. : 0.00 Length:39952 Min. : 0.00 Min. :0.000
1st Qu.: 3.00 Class :character 1st Qu.: 38.00 1st Qu.:2.900
Median : 9.00 Mode :character Median : 57.00 Median :3.300
Mean : 23.14 Mean : 55.51 Mean :3.264
3rd Qu.: 30.00 3rd Qu.: 75.00 3rd Qu.:3.600
Max. :249.00 Max. :100.00 Max. :5.000
NA's :30494 NA's :21936 NA's :21852
tomatoUserReviews tomatoURL DVD
Min. : 0 Length:39952 Min. :1933-06-30
1st Qu.: 46 Class :character 1st Qu.:2000-12-19
Median : 341 Mode :character Median :2004-04-13
Mean : 93049 Mean :2004-01-18
3rd Qu.: 4947 3rd Qu.:2007-02-20
Max. :35794556 Max. :2016-10-04
NA's :15980 NA's :23185
BoxOffice Production Website
Length:39952 Length:39952 Length:39952
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
Response Budget Domestic_Gross
Length:39952 Min. : 1100 Min. : 0
Class :character 1st Qu.: 5000000 1st Qu.: 2453542
Mode :character Median : 18000000 Median : 19539834
Mean : 31952913 Mean : 42934963
3rd Qu.: 40000000 3rd Qu.: 55070326
Max. :425000000 Max. :760507625
NA's :35442 NA's :35442
Gross Date Action Adult
Min. :0.000e+00 Min. :1915 Min. :0.0000 Min. :0.00000
1st Qu.:5.000e+06 1st Qu.:1999 1st Qu.:0.0000 1st Qu.:0.00000
Median :3.006e+07 Median :2005 Median :0.0000 Median :0.00000
Mean :9.057e+07 Mean :2003 Mean :0.1104 Mean :0.01056
3rd Qu.:1.008e+08 3rd Qu.:2011 3rd Qu.:0.0000 3rd Qu.:0.00000
Max. :2.784e+09 Max. :2017 Max. :1.0000 Max. :1.00000
NA's :35442 NA's :35442
Adventure Animation Biography Comedy
Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.0000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.00000 Median :0.00000 Median :0.00000 Median :0.0000
Mean :0.07324 Mean :0.06976 Mean :0.02768 Mean :0.3212
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000
Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.0000
Crime Documentary Drama Family
Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
Mean :0.1014 Mean :0.07624 Mean :0.3963 Mean :0.06638
3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
Fantasy Film-Noir Game-Show History
Min. :0.00000 Min. :0.000000 Min. :0.00e+00 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00e+00 1st Qu.:0.00000
Median :0.00000 Median :0.000000 Median :0.00e+00 Median :0.00000
Mean :0.03502 Mean :0.008811 Mean :5.01e-05 Mean :0.02075
3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00e+00 3rd Qu.:0.00000
Max. :1.00000 Max. :1.000000 Max. :1.00e+00 Max. :1.00000
Horror Music Musical Mystery
Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.06781 Mean :0.02961 Mean :0.03469 Mean :0.04097
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
N/A News Reality-TV
Min. :0.00000 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.00000 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.00000 Median :0.0000000 Median :0.0000000
Mean :0.02468 Mean :0.0005006 Mean :0.0001502
3rd Qu.:0.00000 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.00000 Max. :1.0000000 Max. :1.0000000
Romance Sci-Fi Short Sport
Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
Mean :0.1243 Mean :0.04052 Mean :0.1631 Mean :0.01314
3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
Talk-Show Thriller War Western
Min. :0.0000000 Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.0000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.0000000 Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.0001001 Mean :0.08438 Mean :0.02676 Mean :0.03289
3rd Qu.:0.0000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.0000000 Max. :1.00000 Max. :1.00000 Max. :1.00000
released_month wins nominations Afghanistan
Min. : 1.000 Min. : 0.0000 Min. : 0.000 Min. :0.00e+00
1st Qu.: 4.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.00e+00
Median : 7.000 Median : 0.0000 Median : 0.000 Median :0.00e+00
Mean : 6.624 Mean : 0.7332 Mean : 1.494 Mean :5.01e-05
3rd Qu.:10.000 3rd Qu.: 1.0000 3rd Qu.: 1.000 3rd Qu.:0.00e+00
Max. :12.000 Max. :54.0000 Max. :233.000 Max. :1.00e+00
NA's :4949
Albania Algeria Angola
Min. :0.00e+00 Min. :0.0000000 Min. :0.00e+00
1st Qu.:0.00e+00 1st Qu.:0.0000000 1st Qu.:0.00e+00
Median :0.00e+00 Median :0.0000000 Median :0.00e+00
Mean :7.51e-05 Mean :0.0003004 Mean :5.01e-05
3rd Qu.:0.00e+00 3rd Qu.:0.0000000 3rd Qu.:0.00e+00
Max. :1.00e+00 Max. :1.0000000 Max. :1.00e+00
Antarctica Argentina Armenia
Min. :0.00e+00 Min. :0.000000 Min. :0.0000000
1st Qu.:0.00e+00 1st Qu.:0.000000 1st Qu.:0.0000000
Median :0.00e+00 Median :0.000000 Median :0.0000000
Mean :5.01e-05 Mean :0.005682 Mean :0.0001752
3rd Qu.:0.00e+00 3rd Qu.:0.000000 3rd Qu.:0.0000000
Max. :1.00e+00 Max. :1.000000 Max. :1.0000000
Aruba Australia Austria
Min. :0.00e+00 Min. :0.00000 Min. :0.000000
1st Qu.:0.00e+00 1st Qu.:0.00000 1st Qu.:0.000000
Median :0.00e+00 Median :0.00000 Median :0.000000
Mean :5.01e-05 Mean :0.01685 Mean :0.004806
3rd Qu.:0.00e+00 3rd Qu.:0.00000 3rd Qu.:0.000000
Max. :1.00e+00 Max. :1.00000 Max. :1.000000
Azerbaijan Bahamas Bangladesh
Min. :0.00e+00 Min. :0.0000000 Min. :0.00e+00
1st Qu.:0.00e+00 1st Qu.:0.0000000 1st Qu.:0.00e+00
Median :0.00e+00 Median :0.0000000 Median :0.00e+00
Mean :5.01e-05 Mean :0.0001001 Mean :7.51e-05
3rd Qu.:0.00e+00 3rd Qu.:0.0000000 3rd Qu.:0.00e+00
Max. :1.00e+00 Max. :1.0000000 Max. :1.00e+00
Belarus Belgium Benin
Min. :0.0000000 Min. :0.000000 Min. :0.0e+00
1st Qu.:0.0000000 1st Qu.:0.000000 1st Qu.:0.0e+00
Median :0.0000000 Median :0.000000 Median :0.0e+00
Mean :0.0001252 Mean :0.006758 Mean :2.5e-05
3rd Qu.:0.0000000 3rd Qu.:0.000000 3rd Qu.:0.0e+00
Max. :1.0000000 Max. :1.000000 Max. :1.0e+00
Bermuda Bolivia Bosnia and Herzegovina
Min. :0.0e+00 Min. :0.0e+00 Min. :0.0000000
1st Qu.:0.0e+00 1st Qu.:0.0e+00 1st Qu.:0.0000000
Median :0.0e+00 Median :0.0e+00 Median :0.0000000
Mean :2.5e-05 Mean :2.5e-05 Mean :0.0002503
3rd Qu.:0.0e+00 3rd Qu.:0.0e+00 3rd Qu.:0.0000000
Max. :1.0e+00 Max. :1.0e+00 Max. :1.0000000
Brazil Bulgaria Burkina Faso
Min. :0.000000 Min. :0.000000 Min. :0.0000000
1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000000
Median :0.000000 Median :0.000000 Median :0.0000000
Mean :0.006132 Mean :0.003004 Mean :0.0002753
3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.0000000
Max. :1.000000 Max. :1.000000 Max. :1.0000000
Cambodia Cameroon Canada Chad
Min. :0.0e+00 Min. :0.00e+00 Min. :0.00000 Min. :0.00e+00
1st Qu.:0.0e+00 1st Qu.:0.00e+00 1st Qu.:0.00000 1st Qu.:0.00e+00
Median :0.0e+00 Median :0.00e+00 Median :0.00000 Median :0.00e+00
Mean :2.5e-05 Mean :7.51e-05 Mean :0.04936 Mean :7.51e-05
3rd Qu.:0.0e+00 3rd Qu.:0.00e+00 3rd Qu.:0.00000 3rd Qu.:0.00e+00
Max. :1.0e+00 Max. :1.00e+00 Max. :1.00000 Max. :1.00e+00
Chile China Colombia
Min. :0.000000 Min. :0.000000 Min. :0.0000000
1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000000
Median :0.000000 Median :0.000000 Median :0.0000000
Mean :0.001201 Mean :0.003529 Mean :0.0004005
3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.0000000
Max. :1.000000 Max. :1.000000 Max. :1.0000000
Costa Rica Côte d'Ivoire Croatia
Min. :0.00e+00 Min. :0.0e+00 Min. :0.0000000
1st Qu.:0.00e+00 1st Qu.:0.0e+00 1st Qu.:0.0000000
Median :0.00e+00 Median :0.0e+00 Median :0.0000000
Mean :7.51e-05 Mean :2.5e-05 Mean :0.0006508
3rd Qu.:0.00e+00 3rd Qu.:0.0e+00 3rd Qu.:0.0000000
Max. :1.00e+00 Max. :1.0e+00 Max. :1.0000000
Cuba Cyprus Czech Republic
Min. :0.000000 Min. :0.0000000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.0000000 1st Qu.:0.000000
Median :0.000000 Median :0.0000000 Median :0.000000
Mean :0.001076 Mean :0.0001252 Mean :0.002228
3rd Qu.:0.000000 3rd Qu.:0.0000000 3rd Qu.:0.000000
Max. :1.000000 Max. :1.0000000 Max. :1.000000
Czechoslovakia Denmark Dominican Republic
Min. :0.000000 Min. :0.000000 Min. :0.0000000
1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000000
Median :0.000000 Median :0.000000 Median :0.0000000
Mean :0.002278 Mean :0.009536 Mean :0.0001752
3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.0000000
Max. :1.000000 Max. :1.000000 Max. :1.0000000
East Germany Ecuador Egypt
Min. :0.0000000 Min. :0.00e+00 Min. :0.0000000
1st Qu.:0.0000000 1st Qu.:0.00e+00 1st Qu.:0.0000000
Median :0.0000000 Median :0.00e+00 Median :0.0000000
Mean :0.0009762 Mean :5.01e-05 Mean :0.0007259
3rd Qu.:0.0000000 3rd Qu.:0.00e+00 3rd Qu.:0.0000000
Max. :1.0000000 Max. :1.00e+00 Max. :1.0000000
Estonia Ethiopia Federal Republic of Yugoslavia
Min. :0.0000000 Min. :0.0e+00 Min. :0.0000000
1st Qu.:0.0000000 1st Qu.:0.0e+00 1st Qu.:0.0000000
Median :0.0000000 Median :0.0e+00 Median :0.0000000
Mean :0.0003755 Mean :2.5e-05 Mean :0.0005256
3rd Qu.:0.0000000 3rd Qu.:0.0e+00 3rd Qu.:0.0000000
Max. :1.0000000 Max. :1.0e+00 Max. :1.0000000
Fiji Finland France Gabon
Min. :0.0e+00 Min. :0.000000 Min. :0.00000 Min. :0.0e+00
1st Qu.:0.0e+00 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.0e+00
Median :0.0e+00 Median :0.000000 Median :0.00000 Median :0.0e+00
Mean :2.5e-05 Mean :0.005657 Mean :0.05724 Mean :2.5e-05
3rd Qu.:0.0e+00 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.0e+00
Max. :1.0e+00 Max. :1.000000 Max. :1.00000 Max. :1.0e+00
Georgia Germany Ghana
Min. :0.0000000 Min. :0.00000 Min. :0.0000000
1st Qu.:0.0000000 1st Qu.:0.00000 1st Qu.:0.0000000
Median :0.0000000 Median :0.00000 Median :0.0000000
Mean :0.0002253 Mean :0.03654 Mean :0.0001252
3rd Qu.:0.0000000 3rd Qu.:0.00000 3rd Qu.:0.0000000
Max. :1.0000000 Max. :1.00000 Max. :1.0000000
Gibraltar Greece Guatemala Guinea
Min. :0.0e+00 Min. :0.000000 Min. :0.00e+00 Min. :0.0e+00
1st Qu.:0.0e+00 1st Qu.:0.000000 1st Qu.:0.00e+00 1st Qu.:0.0e+00
Median :0.0e+00 Median :0.000000 Median :0.00e+00 Median :0.0e+00
Mean :2.5e-05 Mean :0.003279 Mean :5.01e-05 Mean :2.5e-05
3rd Qu.:0.0e+00 3rd Qu.:0.000000 3rd Qu.:0.00e+00 3rd Qu.:0.0e+00
Max. :1.0e+00 Max. :1.000000 Max. :1.00e+00 Max. :1.0e+00
Guinea-Bissau Guyana Haiti
Min. :0.00e+00 Min. :0.0e+00 Min. :0.0000000
1st Qu.:0.00e+00 1st Qu.:0.0e+00 1st Qu.:0.0000000
Median :0.00e+00 Median :0.0e+00 Median :0.0000000
Mean :7.51e-05 Mean :2.5e-05 Mean :0.0001001
3rd Qu.:0.00e+00 3rd Qu.:0.0e+00 3rd Qu.:0.0000000
Max. :1.00e+00 Max. :1.0e+00 Max. :1.0000000
Honduras Hong Kong Hungary
Min. :0.0e+00 Min. :0.000000 Min. :0.000000
1st Qu.:0.0e+00 1st Qu.:0.000000 1st Qu.:0.000000
Median :0.0e+00 Median :0.000000 Median :0.000000
Mean :2.5e-05 Mean :0.009912 Mean :0.002929
3rd Qu.:0.0e+00 3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :1.0e+00 Max. :1.000000 Max. :1.000000
Iceland India Indonesia
Min. :0.000000 Min. :0.00000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.000000
Median :0.000000 Median :0.00000 Median :0.000000
Mean :0.000801 Mean :0.01815 Mean :0.001026
3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.000000
Max. :1.000000 Max. :1.00000 Max. :1.000000
Iran Iraq Ireland
Min. :0.000000 Min. :0.00e+00 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.00e+00 1st Qu.:0.000000
Median :0.000000 Median :0.00e+00 Median :0.000000
Mean :0.001902 Mean :7.51e-05 Mean :0.004906
3rd Qu.:0.000000 3rd Qu.:0.00e+00 3rd Qu.:0.000000
Max. :1.000000 Max. :1.00e+00 Max. :1.000000
Isle Of Man Israel Italy
Min. :0.00e+00 Min. :0.000000 Min. :0.00000
1st Qu.:0.00e+00 1st Qu.:0.000000 1st Qu.:0.00000
Median :0.00e+00 Median :0.000000 Median :0.00000
Mean :7.51e-05 Mean :0.004355 Mean :0.03094
3rd Qu.:0.00e+00 3rd Qu.:0.000000 3rd Qu.:0.00000
Max. :1.00e+00 Max. :1.000000 Max. :1.00000
Jamaica Japan Jordan
Min. :0.0000000 Min. :0.0000 Min. :0.00e+00
1st Qu.:0.0000000 1st Qu.:0.0000 1st Qu.:0.00e+00
Median :0.0000000 Median :0.0000 Median :0.00e+00
Mean :0.0002002 Mean :0.0195 Mean :7.51e-05
3rd Qu.:0.0000000 3rd Qu.:0.0000 3rd Qu.:0.00e+00
Max. :1.0000000 Max. :1.0000 Max. :1.00e+00
Kazakhstan Kenya Korea
Min. :0.0000000 Min. :0.0000000 Min. :0.0e+00
1st Qu.:0.0000000 1st Qu.:0.0000000 1st Qu.:0.0e+00
Median :0.0000000 Median :0.0000000 Median :0.0e+00
Mean :0.0002503 Mean :0.0001001 Mean :2.5e-05
3rd Qu.:0.0000000 3rd Qu.:0.0000000 3rd Qu.:0.0e+00
Max. :1.0000000 Max. :1.0000000 Max. :1.0e+00
Kyrgyzstan Latvia Lebanon
Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.00e+00 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.00e+00 Median :0.0000000 Median :0.0000000
Mean :5.01e-05 Mean :0.0001502 Mean :0.0002002
3rd Qu.:0.00e+00 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.00e+00 Max. :1.0000000 Max. :1.0000000
Libya Liechtenstein Lithuania
Min. :0.0e+00 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.0e+00 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.0e+00 Median :0.0000000 Median :0.0000000
Mean :2.5e-05 Mean :0.0002002 Mean :0.0001502
3rd Qu.:0.0e+00 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.0e+00 Max. :1.0000000 Max. :1.0000000
Luxembourg Malaysia Mali
Min. :0.000000 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.000000 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.000000 Median :0.0000000 Median :0.0000000
Mean :0.001402 Mean :0.0004756 Mean :0.0001752
3rd Qu.:0.000000 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.000000 Max. :1.0000000 Max. :1.0000000
Malta Mauritania Mexico
Min. :0.00e+00 Min. :0.00e+00 Min. :0.000000
1st Qu.:0.00e+00 1st Qu.:0.00e+00 1st Qu.:0.000000
Median :0.00e+00 Median :0.00e+00 Median :0.000000
Mean :7.51e-05 Mean :5.01e-05 Mean :0.007359
3rd Qu.:0.00e+00 3rd Qu.:0.00e+00 3rd Qu.:0.000000
Max. :1.00e+00 Max. :1.00e+00 Max. :1.000000
Micronesia Moldova Monaco Mongolia
Min. :0.0e+00 Min. :0.0e+00 Min. :0.00e+00 Min. :0.0e+00
1st Qu.:0.0e+00 1st Qu.:0.0e+00 1st Qu.:0.00e+00 1st Qu.:0.0e+00
Median :0.0e+00 Median :0.0e+00 Median :0.00e+00 Median :0.0e+00
Mean :2.5e-05 Mean :2.5e-05 Mean :5.01e-05 Mean :2.5e-05
3rd Qu.:0.0e+00 3rd Qu.:0.0e+00 3rd Qu.:0.00e+00 3rd Qu.:0.0e+00
Max. :1.0e+00 Max. :1.0e+00 Max. :1.00e+00 Max. :1.0e+00
Montenegro Morocco N/A
Min. :0.0e+00 Min. :0.0000000 Min. :0.00000
1st Qu.:0.0e+00 1st Qu.:0.0000000 1st Qu.:0.00000
Median :0.0e+00 Median :0.0000000 Median :0.00000
Mean :2.5e-05 Mean :0.0004756 Mean :0.01407
3rd Qu.:0.0e+00 3rd Qu.:0.0000000 3rd Qu.:0.00000
Max. :1.0e+00 Max. :1.0000000 Max. :1.00000
Namibia Nepal Netherlands
Min. :0.00e+00 Min. :0.0e+00 Min. :0.000000
1st Qu.:0.00e+00 1st Qu.:0.0e+00 1st Qu.:0.000000
Median :0.00e+00 Median :0.0e+00 Median :0.000000
Mean :5.01e-05 Mean :2.5e-05 Mean :0.009687
3rd Qu.:0.00e+00 3rd Qu.:0.0e+00 3rd Qu.:0.000000
Max. :1.00e+00 Max. :1.0e+00 Max. :1.000000
Netherlands Antilles New Zealand Nicaragua
Min. :0.0e+00 Min. :0.000000 Min. :0.0e+00
1st Qu.:0.0e+00 1st Qu.:0.000000 1st Qu.:0.0e+00
Median :0.0e+00 Median :0.000000 Median :0.0e+00
Mean :2.5e-05 Mean :0.003104 Mean :2.5e-05
3rd Qu.:0.0e+00 3rd Qu.:0.000000 3rd Qu.:0.0e+00
Max. :1.0e+00 Max. :1.000000 Max. :1.0e+00
Niger Nigeria North Korea
Min. :0.0e+00 Min. :0.0000000 Min. :0.0e+00
1st Qu.:0.0e+00 1st Qu.:0.0000000 1st Qu.:0.0e+00
Median :0.0e+00 Median :0.0000000 Median :0.0e+00
Mean :2.5e-05 Mean :0.0001001 Mean :2.5e-05
3rd Qu.:0.0e+00 3rd Qu.:0.0000000 3rd Qu.:0.0e+00
Max. :1.0e+00 Max. :1.0000000 Max. :1.0e+00
Norway Pakistan Palestine
Min. :0.000000 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.000000 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.000000 Median :0.0000000 Median :0.0000000
Mean :0.005081 Mean :0.0001252 Mean :0.0002503
3rd Qu.:0.000000 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.000000 Max. :1.0000000 Max. :1.0000000
Panama Papua New Guinea Paraguay
Min. :0.0000000 Min. :0.00e+00 Min. :0.0e+00
1st Qu.:0.0000000 1st Qu.:0.00e+00 1st Qu.:0.0e+00
Median :0.0000000 Median :0.00e+00 Median :0.0e+00
Mean :0.0001752 Mean :5.01e-05 Mean :2.5e-05
3rd Qu.:0.0000000 3rd Qu.:0.00e+00 3rd Qu.:0.0e+00
Max. :1.0000000 Max. :1.00e+00 Max. :1.0e+00
Peru Philippines Poland
Min. :0.0000000 Min. :0.00000 Min. :0.000000
1st Qu.:0.0000000 1st Qu.:0.00000 1st Qu.:0.000000
Median :0.0000000 Median :0.00000 Median :0.000000
Mean :0.0006007 Mean :0.00423 Mean :0.009036
3rd Qu.:0.0000000 3rd Qu.:0.00000 3rd Qu.:0.000000
Max. :1.0000000 Max. :1.00000 Max. :1.000000
Portugal Puerto Rico Qatar
Min. :0.000000 Min. :0.0000000 Min. :0.00e+00
1st Qu.:0.000000 1st Qu.:0.0000000 1st Qu.:0.00e+00
Median :0.000000 Median :0.0000000 Median :0.00e+00
Mean :0.001927 Mean :0.0002002 Mean :5.01e-05
3rd Qu.:0.000000 3rd Qu.:0.0000000 3rd Qu.:0.00e+00
Max. :1.000000 Max. :1.0000000 Max. :1.00e+00
Republic of Macedonia Romania Russia
Min. :0.0000000 Min. :0.000000 Min. :0.000000
1st Qu.:0.0000000 1st Qu.:0.000000 1st Qu.:0.000000
Median :0.0000000 Median :0.000000 Median :0.000000
Mean :0.0001001 Mean :0.002478 Mean :0.004956
3rd Qu.:0.0000000 3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :1.0000000 Max. :1.000000 Max. :1.000000
Saudi Arabia Senegal Serbia
Min. :0.00e+00 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.00e+00 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.00e+00 Median :0.0000000 Median :0.0000000
Mean :5.01e-05 Mean :0.0003504 Mean :0.0001001
3rd Qu.:0.00e+00 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.00e+00 Max. :1.0000000 Max. :1.0000000
Serbia and Montenegro Singapore Slovakia
Min. :0.0e+00 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.0e+00 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.0e+00 Median :0.0000000 Median :0.0000000
Mean :2.5e-05 Mean :0.0005256 Mean :0.0004005
3rd Qu.:0.0e+00 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.0e+00 Max. :1.0000000 Max. :1.0000000
Slovenia Solomon Islands South Africa
Min. :0.0000000 Min. :0.0e+00 Min. :0.000000
1st Qu.:0.0000000 1st Qu.:0.0e+00 1st Qu.:0.000000
Median :0.0000000 Median :0.0e+00 Median :0.000000
Mean :0.0005757 Mean :2.5e-05 Mean :0.003229
3rd Qu.:0.0000000 3rd Qu.:0.0e+00 3rd Qu.:0.000000
Max. :1.0000000 Max. :1.0e+00 Max. :1.000000
South Korea Soviet Union Spain Sweden
Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000
Median :0.000000 Median :0.00000 Median :0.00000 Median :0.000000
Mean :0.004606 Mean :0.01079 Mean :0.01785 Mean :0.009962
3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000
Max. :1.000000 Max. :1.00000 Max. :1.00000 Max. :1.000000
Switzerland Syria Taiwan
Min. :0.000000 Min. :0.0e+00 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.0e+00 1st Qu.:0.000000
Median :0.000000 Median :0.0e+00 Median :0.000000
Mean :0.006082 Mean :2.5e-05 Mean :0.002128
3rd Qu.:0.000000 3rd Qu.:0.0e+00 3rd Qu.:0.000000
Max. :1.000000 Max. :1.0e+00 Max. :1.000000
Tajikistan Tanzania Thailand
Min. :0.00e+00 Min. :0.00e+00 Min. :0.000000
1st Qu.:0.00e+00 1st Qu.:0.00e+00 1st Qu.:0.000000
Median :0.00e+00 Median :0.00e+00 Median :0.000000
Mean :7.51e-05 Mean :5.01e-05 Mean :0.001126
3rd Qu.:0.00e+00 3rd Qu.:0.00e+00 3rd Qu.:0.000000
Max. :1.00e+00 Max. :1.00e+00 Max. :1.000000
The Democratic Republic Of Congo Trinidad and Tobago Tunisia
Min. :0.0e+00 Min. :0.00e+00 Min. :0.0000000
1st Qu.:0.0e+00 1st Qu.:0.00e+00 1st Qu.:0.0000000
Median :0.0e+00 Median :0.00e+00 Median :0.0000000
Mean :2.5e-05 Mean :5.01e-05 Mean :0.0003504
3rd Qu.:0.0e+00 3rd Qu.:0.00e+00 3rd Qu.:0.0000000
Max. :1.0e+00 Max. :1.00e+00 Max. :1.0000000
Turkey Turkmenistan Turks And Caicos Islands
Min. :0.000000 Min. :0.0e+00 Min. :0.0e+00
1st Qu.:0.000000 1st Qu.:0.0e+00 1st Qu.:0.0e+00
Median :0.000000 Median :0.0e+00 Median :0.0e+00
Mean :0.002228 Mean :2.5e-05 Mean :2.5e-05
3rd Qu.:0.000000 3rd Qu.:0.0e+00 3rd Qu.:0.0e+00
Max. :1.000000 Max. :1.0e+00 Max. :1.0e+00
UK Ukraine United Arab Emirates
Min. :0.00000 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.00000 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.00000 Median :0.0000000 Median :0.0000000
Mean :0.09078 Mean :0.0004255 Mean :0.0005507
3rd Qu.:0.00000 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.00000 Max. :1.0000000 Max. :1.0000000
Uruguay USA Uzbekistan
Min. :0.0000000 Min. :0.0000 Min. :0.0000000
1st Qu.:0.0000000 1st Qu.:0.0000 1st Qu.:0.0000000
Median :0.0000000 Median :1.0000 Median :0.0000000
Mean :0.0002253 Mean :0.6264 Mean :0.0001001
3rd Qu.:0.0000000 3rd Qu.:1.0000 3rd Qu.:0.0000000
Max. :1.0000000 Max. :1.0000 Max. :1.0000000
Vanuatu Venezuela Vietnam
Min. :0.0e+00 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.0e+00 1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.0e+00 Median :0.0000000 Median :0.0000000
Mean :2.5e-05 Mean :0.0002753 Mean :0.0001252
3rd Qu.:0.0e+00 3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.0e+00 Max. :1.0000000 Max. :1.0000000
West Germany Yugoslavia Zaire
Min. :0.00000 Min. :0.000000 Min. :0.00e+00
1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00e+00
Median :0.00000 Median :0.000000 Median :0.00e+00
Mean :0.01209 Mean :0.004881 Mean :5.01e-05
3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00e+00
Max. :1.00000 Max. :1.000000 Max. :1.00e+00
Zimbabwe
Min. :0.0000000
1st Qu.:0.0000000
Median :0.0000000
Mean :0.0001001
3rd Qu.:0.0000000
Max. :1.0000000
# cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')
# Remove column from dataframe
df$Country = NULL
# cols = setdiff(cols, 'Genre')
colCount = colSums(df[, 80:ncol(df)])
topTenIds = order(colCount,decreasing=TRUE)[1:70] + 1
topTenCols = names(df[, 80:ncol(df)][topTenIds])
# The top 30 most common countries are
print(topTenCols) [1] "Uzbekistan" "Ukraine"
[3] "Gabon" "Chad"
[5] "Ghana" "Jamaica"
[7] "Jordan" "Indonesia"
[9] "Sweden" "Namibia"
[11] "Yugoslavia" "Spain"
[13] "Switzerland" "Hungary"
[15] "Netherlands Antilles" "Dominican Republic"
[17] "Portugal" "Micronesia"
[19] "Benin" "Bulgaria"
[21] "Syria" "France"
[23] "Pakistan" "Saudi Arabia"
[25] "Isle Of Man" "Zaire"
[27] "Soviet Union" "Italy"
[29] "Poland" "Colombia"
[31] "Guatemala" "South Korea"
[33] "Nicaragua" "Burkina Faso"
[35] "Iceland" "Russia"
[37] "Denmark" "Czechoslovakia"
[39] "Turkmenistan" "Tajikistan"
[41] "Puerto Rico" "Iraq"
[43] "Malaysia" "China"
[45] "The Democratic Republic Of Congo" "Cyprus"
[47] "Iran" "Ecuador"
[49] "India" "Estonia"
[51] "Cuba" "Philippines"
[53] "Solomon Islands" "Uruguay"
[55] "Fiji" "Slovakia"
[57] "Mali" "N/A"
[59] "United Arab Emirates" "Costa Rica"
[61] "Slovenia" "Ethiopia"
[63] "Serbia" "Turkey"
[65] "Cambodia" "Vietnam"
[67] "Brazil" "Kenya"
[69] "Panama" "Germany" cols = c('Title', 'Runtime', 'Gross', 'Domestic_Gross')
cols = c(cols, topTenCols)
df9 = df[cols]
df9.long = melt(df9, id.vars=c('Title', 'Runtime', 'Gross', 'Domestic_Gross'), variable.name='country')
df9.long = df9.long[apply(df9.long['value'],1,function(z) !any(z==0)),]
print(summary(df9.long)) Title Runtime Gross
Length:11296 Min. : 1.00 Min. :0.000e+00
Class :character 1st Qu.: 87.00 1st Qu.:5.760e+06
Mode :character Median : 96.00 Median :2.610e+07
Mean : 96.96 Mean :7.038e+07
3rd Qu.:109.00 3rd Qu.:9.151e+07
Max. :873.00 Max. :1.215e+09
NA's :420 NA's :10216
Domestic_Gross country value
Min. : 0 France :2287 Min. :1
1st Qu.: 859244 Germany:1460 1st Qu.:1
Median : 10015778 Italy :1236 Median :1
Mean : 28474360 N/A : 986 Mean :1
3rd Qu.: 38420602 India : 725 3rd Qu.:1
Max. :408992272 Spain : 713 Max. :1
NA's :10216 (Other):3889 print(head(df9.long)) Title Runtime Gross Domestic_Gross country value
3188 Aprel May 93 NA NA Uzbekistan 1
3356 Cinedictum 40 NA NA Uzbekistan 1
8067 Luna Papa 107 NA NA Uzbekistan 1
8834 Bo Ba Bu 85 NA NA Uzbekistan 1
41923 Divan 90 NA NA Ukraine 1
42383 Mamay 80 NA NA Ukraine 1ggplot(df9.long, aes(as.factor(country), Runtime)) +
geom_boxplot() +
coord_flip() +
scale_x_discrete("country")Warning: Removed 420 rows containing non-finite values (stat_boxplot).
ggplot(df9.long, aes(as.factor(country), Gross)) +
geom_boxplot() +
coord_flip() +
scale_x_discrete("country")Warning: Removed 10216 rows containing non-finite values (stat_boxplot).
ggplot(df9.long, aes(as.factor(country), Domestic_Gross)) +
geom_boxplot() +
coord_flip() +
scale_x_discrete("country")Warning: Removed 10216 rows containing non-finite values (stat_boxplot).
Q: Expected insight #1.
A: First we plot runtime by country. While there is different distribution and sample size by country, notice that the median runtime by country is actually really similar. I would expect this because I am not aware of differences in runtime trends by country.
Q: Expected insight #2.
A:
Plotting the Gross Revenue by country, one expected insight is that lower income countries tend to have on average lower distribution of Gross Revenue.
10. Unexpected insight
Come up with one new insight (backed up by data and graphs) that is unexpected at first glance and do your best to motivate it. Same instructions apply as the previous task.
# TODO: Find and illustrate one unexpected insight
df$made_to_dvd = as.numeric(!is.na(df$DVD))
ggplot(df, aes(imdbRating, made_to_dvd)) +
stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")Warning: Removed 1134 rows containing non-finite values (stat_summary).
Q: Unexpected insight.
A: One thing that was unexpected to me was I would have expected that there would be a positive correlation between ratings and whether the move was made to a DVD (better movies would more likely be made into a DVD). The relationship I found was somewhat of an “inversed-U” shape. At very low ratings, the proportion of DVD’s made is very low. Suprisingly, highly rated ratings also have lower made to DVD ratings.
---
title: "Project 1: Explore and Prepare Data"
subtitle: "CSE6242 - Data and Visual Analytics - Fall 2017\n\nDue: Sunday, October 15, 2017 at 11:59 PM UTC-12:00 on T-Square"
author: Vincent La (Georgia Tech ID - vla6)
date: October 12, 2017
output: html_notebook
---

_Note: This project involves getting data ready for analysis and doing some preliminary investigations. Project 2 will involve modeling and predictions on the same dataset, and will be released at a later date. Both projects will have equal weightage towards your grade. You may reuse some of the preprocessing/analysis steps from Project 1 in Project 2._

# Data

In this project, you will explore a dataset that contains information about movies, including ratings, budget, gross revenue and other attributes. It was prepared by Dr. Guy Lebanon, and here is his description of the dataset:

> The file [`movies_merged`](https://s3.amazonaws.com/content.udacity-data.com/courses/gt-cs6242/project/movies_merged) contains a dataframe with the same name that has 40K rows and 39 columns. Each row represents a movie title and each column represents a descriptor such as `Title`, `Actors`, and `Budget`. I collected the data by querying IMDb’s API (see [www.omdbapi.com](http://www.omdbapi.com/)) and joining it with a separate dataset of movie budgets and gross earnings (unknown to you). The join key was the movie title. This data is available for personal use, but IMDb’s terms of service do not allow it to be used for commercial purposes or for creating a competing repository.

# Objective

Your goal is to investigate the relationship between the movie descriptors and the box office success of movies, as represented by the variable `Gross`. This task is extremely important as it can help a studio decide which titles to fund for production, how much to bid on produced movies, when to release a title, how much to invest in marketing and PR, etc. This information is most useful before a title is released, but it is still very valuable after the movie is already released to the public (for example it can affect additional marketing spend or how much a studio should negotiate with on-demand streaming companies for “second window” streaming rights).

# Instructions

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. Open this file in RStudio to get started.

When you execute code within the notebook, the results appear beneath the code. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*.

```{r}
# Project 1: http://cse6242.gatech.edu/fall-2017/pr1/
# To compile R Markdown in terminal run: Rscript -e "rmarkdown::render('pr1.Rmd', clean=TRUE)"
x = 1:10
print(x^2)
```

Plots appear inline too:
```{r}
plot(x, x^2, 'o')
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*. Enter some R code and run it.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file).

Please complete all the tasks below by implementing code chunks that have a `TODO` comment in them, running all code chunks so that output and plots are displayed, and typing in answers to each question (**Q:** ...) next to/below the corresponding answer prompt (**A:**). Feel free to add code chunks/show additional output to support any of the answers.

When you are done, you will need to submit the final R markdown file (as **pr1.Rmd**) with all code chunks implemented and executed, and all text responses written in. You also need to submit a PDF export of the markdown file (as **pr1.pdf**), which should show your code, output, plots and written responses--this will be your project report. Compress these two files into a single .zip archive and upload it on T-Square.

# Setup

## Load data

Make sure you've downloaded the [`movies_merged`](https://s3.amazonaws.com/content.udacity-data.com/courses/gt-cs6242/project/movies_merged) file and it is in the current working directory. Now load it into memory:

```{r}
load('movies_merged')
cat("Dataset has", dim(movies_merged)[1], "rows and", dim(movies_merged)[2], "columns", end="\n", file="")
```

This creates an object of the same name (`movies_merged`). For convenience, you can copy it to `df` and start using it:

```{r}
df = movies_merged
cat("Column names:", end="\n", file="")
colnames(df)
```

## Load R packages

Load any R packages that you will need to use. You can come back to this chunk, edit it and re-run to load any additional packages later.

```{r}
library(ggplot2)
library(GGally)

# Note that I'm using Anaconda R, and I was having trouble installing qdapTools. I'm not sure
# if this is the same on other machines but I did have to install R XML separately
# https://anaconda.org/r/r-xml
# conda install -c r r-xml. Only after could I install qdapTools.
library(qdapTools)
```

If you are loading any non-standard packages (ones that have not been discussed in class or explicitly allowed for this project), please mention them below. Include any special instructions if they cannot be installed using the regular `install.packages('<pkg name>')` command.

**Non-standard packages used**: None

# Tasks

Each task below is worth **10** points, and is meant to be performed sequentially, i.e. do step 2 after you have processed the data as described in step 1. Total points: **100**

Complete each task by implementing code chunks as described by `TODO` comments, and by responding to questions ("**Q**:") with written answers ("**A**:"). If you are unable to find a meaningful or strong relationship in any of the cases when requested, explain why not by referring to appropriate plots/statistics.

It is okay to handle missing values below by omission, but please omit as little as possible. It is worthwhile to invest in reusable and clear code as you may need to use it or modify it in project 2.

## 1. Remove non-movie rows

The variable `Type` captures whether the row is a movie, a TV series, or a game. Remove all rows from `df` that do not correspond to movies.

```{r}
# TODO: Remove all rows from df that do not correspond to movies
df2 <- df[df$Type == "movie",]
original_dim = dim(df)
new_dim = dim(df2)

df = df2
# Differences in rows
print(original_dim[1] - new_dim[1])
```

**Q**: How many rows are left after removal? _Enter your response below._

**A**:
The number of rows left after removal are `r new_dim[1]`.

## 2. Process `Runtime` column

The variable `Runtime` represents the length of the title as a string. Write R code to convert it to a numeric value (in minutes) and replace `df$Runtime` with the new numeric column.

```{r}
extract_runtime = function(r){
    times = unlist(r)
    minutes = 0
    for (i in 1:length(times) - 1){
        if (times[i + 1] == 'h'){
            minutes = minutes + as.numeric(times[i]) * 60
        } else if (times[i + 1] == 'min'){
            minutes = minutes + as.numeric(times[i])
        }
    }
    if (minutes == 0){
        return(NA)
    } else{
        return(minutes)
    }
}

y=strsplit(df$Runtime,' ')
new_runtimes = unlist(lapply(y, extract_runtime))
df$Runtime = new_runtimes

cols = c('Runtime', 'Year', 'Budget')
summary(df[cols])
```

Now investigate the distribution of `Runtime` values and how it changes over years (variable `Year`, which you can bucket into decades) and in relation to the budget (variable `Budget`). Include any plots that illustrate.

```{r}
# TODO: Investigate the distribution of Runtime values and how it varies by Year and Budget
ggplot(df, aes(x=Runtime)) +
  geom_histogram() +
  ggtitle('Histogram of Runtime (minutes)')

ggplot(df, aes(Year, Runtime)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(Budget, Runtime)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

_Feel free to insert additional code chunks as necessary._

**Q**: Comment on the distribution as well as relationships. Are there any patterns or trends that you can observe?

**A**:

Looking in the aggregate, it seems like most runtimes are under 2 hours. There is a clear relationship between run time and year, looking a bit like a sigmoid function. This makes sense as very early years, it was probably technologically prohibitive to create long movies. As technology improved, runtimes increased, but there's only so much time you can expect to hold an audience captive. Interestingly enough, during the 1990's there seems to be a downward trend in runtimes until around mid 2000's where it picked up again.

## 3. Encode `Genre` column

The column `Genre` represents a list of genres associated with the movie in a string format. Write code to parse each text string into a binary vector with 1s representing the presence of a genre and 0s the absence, and add it to the dataframe as additional columns. Then remove the original `Genre` column.

For example, if there are a total of 3 genres: Drama, Comedy, and Action, a movie that is both Action and Comedy should be represented by a binary vector <0, 1, 1>. Note that you need to first compile a dictionary of all possible genres and then figure out which movie has which genres (you can use the R `tm` package to create the dictionary).

```{r}
# TODO: Replace Genre with a collection of binary columns
require(tm)

# All unique genres in the dataframe
print(unique(unlist(strsplit(df$Genre, ', '))))

# Example of how to one-hot encode: https://stackoverflow.com/questions/39778387/r-dataframe-one-hot-encoding-of-column-containing-multiple-terms

df = cbind(df, mtabulate(strsplit(df$Genre, ", ")))

cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')

# Remove column from dataframe
df$Genre = NULL
```

Plot the relative proportions of movies having the top 10 most common genres.

```{r}
# TODO: Select movies from top 10 most common genres and plot their relative proportions

# See https://stackoverflow.com/questions/20679702/r-find-column-with-the-largest-column-sum as reference
cols = setdiff(cols, 'Genre')
colCount = colSums(df[cols])
topTenIds = order(colCount,decreasing=TRUE)[1:10] + 1
topTenCols = names(df[cols][topTenIds])

# The top 10 most common genres are
print(topTenCols)

# Probably need to do some manual "stacking" of dataframes by each of the top 10 most common genres.
# Note that I was somewhat confused on what "relative proportions of movies" meant. I found this helpful
# https://piazza.com/class/j6gt7ycx6nk145?cid=396
# Suppose you have ten ice creams.  Let's say that four of them are Chocolate ice cream.  What is the relative proportion of Chocolate ice cream ?   --> 4 / 10
cols3 = c('Title', 'Runtime', topTenCols)
df3 = df[cols3]
print(summary(df3))
print(head(df3))

library(reshape2)
df3.long = melt(df3, id.vars=c('Title', 'Runtime'))

# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df3.long = df3.long[apply(df3.long['value'],1,function(z) !any(z==0)),]
print(summary(df3.long))
print(head(df3.long))

# Finally plot relative proportions
# https://sebastiansauer.github.io/percentage_plot_ggplot2_V2/
ggplot(df3.long, aes(x=variable)) +
  geom_bar(aes(y = (..count..)/sum(..count..))) +
  ylab('Relative Proportion')
```

Examine how the distribution of `Runtime` changes across genres for the top 10 most common genres.

```{r}
# TODO: Plot Runtime distribution for top 10 most common genres
# ggplot(df3.long, aes(x=Runtime)) +
#   geom_density(aes(color=variable, group=variable)) +
#   ggtitle('Distribution of Runtimes by Movie Genre')

ggplot(df3.long, aes(as.factor(variable), Runtime)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("Genre")
```

**Q**: Describe the interesting relationship(s) you observe. Are there any expected or unexpected trends that are evident?

**A**:
One thing that immediately stood out to me was that in looking at the extreme outliers (Runtimes greater than 300 minutes for example or 5 hours), most of these very long movies tend to be Drama, War, and Documentaries, which fits anecdotally with my priors of what movies would be extremely long.

Animation films tend to be the shortest, which is expected as there are probably many short animation films. I was actually surprised to see that most animation films are very very short.

One that that was unexpected to me was that with the exception of Animation and Family films, most of the median runtimes for all the rest of the genres are very similar.

## 4. Eliminate mismatched rows

The dataframe was put together by merging two different sources of data and it is possible that the merging process was inaccurate in some cases (the merge was done based on movie title, but there are cases of different movies with the same title). There are 3 columns that contain date information: `Year` (numeric year), `Date` (numeric year), and `Released` (string representation of the release date).

Find and remove all rows where you suspect a merge error occurred based on a mismatch between these variables. To make sure subsequent analysis and modeling work well, avoid removing more than 10% of the rows that have a `Gross` value present.

_Note: Do not remove the rows with `Gross == NA` at this point, just use this a guideline._

```{r}
# TODO: Remove rows with Year/Date/Released mismatch
library(lubridate)  # Used to extract year from Released

cols4 = c('Year', 'Date', 'Released', 'Gross')
df4 = df[cols4]
df4$released_year = year(df4$Released)
df4$not_matched = (df4$Year != df4$released_year) & (df4$Year != df4$Date) & (df4$Date != df4$released_year)

# Number of rows with non-null Gross Value
print(nrow(df[which(!is.na(df$Gross)), ]))

# Printing rows in the original data set
print(nrow(df))

df = df[which(df4$not_matched == FALSE | is.na(df4$not_matched)), ]
print(nrow(df))
```

**Q**: What is your precise removal logic, and how many rows remain in the resulting dataset?

**A**:
Out of the 40,000 original rows, I removed rows where `Year != released_year` and `Year != Date` and `Date != released_year` This resulted in `r nrow(df4[which(df4$not_matched == TRUE & !is.na(df4$Gross)), ])` rows to be removed where there was a non-null `Gross` value. Since there were originally `r nrow(df[which(!is.na(df$Gross)), ])` rows with a non-null `Gross` value, we have indeed removed less than 10 percent of the rows with non-null `Gross` values.

There are now `r nrow(df)` rows in the resulting dataset.

## 5. Explore `Gross` revenue

For the commercial success of a movie, production houses want to maximize Gross revenue. Investigate if Gross revenue is related to Budget, Runtime or Genre in any way.

_Note: To get a meaningful relationship, you may have to partition the movies into subsets such as short vs. long duration, or by genre, etc._

```{r}
# TODO: Investigate if Gross Revenue is related to Budget, Runtime or Genre
cols = c('Title', 'Gross', 'Runtime', 'Budget', 'Released', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')
df5 = df[cols]

print(summary(df5))
print(head(df5))

ggplot(df, aes(Runtime, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(Budget, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

# Next melt the df to get genre as one column (like in question 3) and plot grouped by genre
df5.long = melt(df5, id.vars=c('Title', 'Gross', 'Runtime', 'Budget', 'Released'), variable.name='genre')

# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df5.long = df5.long[apply(df5.long['value'], 1, function(z) !any(z==0)),]

library(plyr)
df5.long.runtime <- ddply(df5.long, c('Runtime', 'genre'), function(x) mean(x$Gross))
names(df5.long.runtime)[names(df5.long.runtime) == 'V1'] <- 'Gross'
df5.long.runtime = df5.long.runtime[!df5.long.runtime$Runtime > 180,]  # Removing movies greater than 3 hours

df5.long.budget <- ddply(df5.long, c('Budget', 'genre'), function(x) mean(x$Gross))
names(df5.long.budget)[names(df5.long.budget) == 'V1'] <- 'Gross'

ggplot(df5.long.runtime, aes(Runtime, Gross)) +
  geom_point(aes(color=genre, group=genre))

ggplot(df5.long.budget, aes(Budget, Gross)) +
  geom_point(aes(color=genre, group=genre))
```

**Q**: Did you find any observable relationships or combinations of Budget/Runtime/Genre that result in high Gross revenue? If you divided the movies into different subsets, you may get different answers for them - point out interesting ones.

**A**:

Yes, it seems that in the aggregate, Gross revenue is positive correlated with Runtime, but only to a certain extent. After about 3 hours or so, the relationship is much murkier. Gross Revenue seems generally positively correlated with Budget.

```{r}
# TODO: Investigate if Gross Revenue is related to Release Month
print(summary(df5.long))
print(head(df5.long))
df$released_month = month(df$Released)
ggplot(df, aes(released_month, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

There is definitely a relationship of Gross Revenue and release month. Summer month movies and holiday movies gross significantly more revenue than movies released in other months.

## 6. Process `Awards` column

The variable `Awards` describes nominations and awards in text format. Convert it to 2 numeric columns, the first capturing the number of wins, and the second capturing nominations. Replace the `Awards` column with these new columns, and then study the relationship of `Gross` revenue with respect to them.

_Note: The format of the `Awards` column is not standard; you may have to use regular expressions to find the relevant values. Try your best to process them, and you may leave the ones that don't have enough information as NAs or set them to 0s._

```{r}
# TODO: Convert Awards to 2 numeric columns: wins and nominations
library(stringi)
cols = c('Title', 'Awards', 'Gross')
df6 = df[cols]
print(unique(df6$Awards))

print(summary(df6))
print(head(df6))

awards = stri_extract_all(df6$Awards, regex="\\d+")
award_labels = stri_extract_all(df6$Awards, regex="win|won|nomin")
print(head(awards))
print(head(award_labels))

wins_list = c()
nominations_list = c()

for (i in 1:length(awards)){
    wins = 0
    nominations = 0
    for(j in 1:length(awards[[i]])){
        if(!is.na(award_labels[[i]][j]) & (award_labels[[i]][j] == 'wins' | award_labels[[i]][j] == 'win' | award_labels[[i]][j] == 'won')){
            wins = wins + as.numeric(awards[[i]][j])
        } else if (!is.na(award_labels[[i]][j]) & award_labels[[i]][j] == 'nomin'){
            nominations = nominations + as.numeric(awards[[i]][j])
        }
    }
    wins_list = c(wins_list, wins)
    nominations_list = c(nominations_list, nominations)
}

df$wins = wins_list
df$nominations = nominations_list

print(head(df[c('Awards', 'wins', 'nominations')], 20))
```

**Q**: How did you construct your conversion mechanism? How many rows had valid/non-zero wins or nominations?

**A**:
To construct the conversion mechanism, I first used `stri_extract_all` to extract all numerical substrings. I then extracted all instances of "win, won, or nomin". I then assigned the numerical to however it corresponded to the position of "win, won, or nomin".


```{r}
# TODO: Plot Gross revenue against wins and nominations
ggplot(df, aes(wins, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(nominations, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

**Q**: How does the gross revenue vary by number of awards won and nominations received?

**A**:
Surprisingly, there does not seem to be much of a relationship between gross revenue and awards won.


## 7. Movie ratings from IMDb and Rotten Tomatoes

There are several variables that describe ratings, including IMDb ratings (`imdbRating` represents average user ratings and `imdbVotes` represents the number of user ratings), and multiple Rotten Tomatoes ratings (represented by several variables pre-fixed by `tomato`). Read up on such ratings on the web (for example [rottentomatoes.com/about](https://www.rottentomatoes.com/about) and [ www.imdb.com/help/show_leaf?votestopfaq](http:// www.imdb.com/help/show_leaf?votestopfaq)).

Investigate the pairwise relationships between these different descriptors using graphs.

```{r}
library(GGally)

# TODO: Illustrate how ratings from IMDb and Rotten Tomatoes are related
cols = c('imdbRating', 'imdbVotes', 'tomatoMeter', 'tomatoImage', 'tomatoRating', 'tomatoReviews',
         'tomatoFresh', 'tomatoRotten', 'tomatoConsensus', 'tomatoUserMeter', 'tomatoUserRating',
         'tomatoUserReviews')
df7 = df[cols]

print(summary(df7))
print(head(df7))

ggpairs(df7, columns=c('imdbRating', 'tomatoMeter', 'tomatoRating'), aes())
```

**Q**: Comment on the similarities and differences between the user ratings of IMDb and the critics ratings of Rotten Tomatoes.

**A**:
First off, it's interesting to note that we have significantly more data of IMDB ratings than Rotten tomatoes. In our data, we have about 1100 movies with missing IMDB ratings. However, we have close to 30,000 movies with missing Rotten Tomatoes ratings.

In the plot above we plot the `ggpairs` of `imdbRating`, `tomatoMeter`, and `tomatoRating`. Notice that the correlation between `tomatoMeter` and `tomatoRating` is very high, at 0.94. This makes sense as it's from the same source. The correlation between `tomatoMeter` and `imdbRating` is a bit lower, at around 0.746 vs 0.794 which is the correlation between `tomatoRating` and `imdbRating`.

Looking at the summary of the variables as well, it looks like `imdbRating` and `tomatoRating` are rated on similar scales. `imdbRating` looks like it's rated on a scale of 1 to 10, while `tomatoRating` looks like it's rated on a scale of 0 to 9.8. However, `tomatoMeter` looks like it's scaled from 0 to 100.

## 8. Ratings and awards

These ratings typically reflect the general appeal of the movie to the public or gather opinions from a larger body of critics. Whereas awards are given by professional societies that may evaluate a movie on specific attributes, such as artistic performance, screenplay, sound design, etc.

Study the relationship between ratings and awards using graphs (awards here refers to wins and/or nominations).

```{r}
# TODO: Show how ratings and awards are related
ggplot(df, aes(imdbRating, wins)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(imdbRating, nominations)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(tomatoRating, wins)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(tomatoRating, nominations)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")


mod1 = lm(wins ~ imdbRating, data = df)
summary(mod1)

mod1 = lm(nominations ~ imdbRating, data = df)
summary(mod1)

mod1 = lm(wins ~ tomatoRating, data = df)
summary(mod1)

mod1 = lm(nominations ~ tomatoRating, data = df)
summary(mod1)
```

**Q**: How good are these ratings in terms of predicting the success of a movie in winning awards or nominations? Is there a high correlation between two variables?

**A**:
One extremely interesting trend from the graph above is that for both `imdbRatings` and `tomatoRatings` generally there is a positive correlation between ratings and both wins and nominations. But very interestingly enough, the relationship is actually not monotonic. In the extremely upper regions of ratings, the number of wins and nominations actually decrease!

I fit a linear regression between wins and nominations against ratings field. Notice that in each regression the coefficient is significant, which means that there is a significant positive relationship between rating and nominations and wins. However, the R^2 is relatively low, so the correlation is relatively low.

## 9. Expected insights

Come up with two new insights (backed up by data and graphs) that is expected. Here “new” means insights that are not an immediate consequence of one of the above tasks. You may use any of the columns already explored above or a different one in the dataset, such as `Title`, `Actors`, etc.

```{r}
# TODO: Find and illustrate two expected insights
df = cbind(df, mtabulate(strsplit(df$Country, ", ")))
print(summary(df))

# cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')

# Remove column from dataframe
df$Country = NULL

# cols = setdiff(cols, 'Genre')
colCount = colSums(df[, 80:ncol(df)])
topTenIds = order(colCount,decreasing=TRUE)[1:70] + 1
topTenCols = names(df[, 80:ncol(df)][topTenIds])

# The top 30 most common countries are
print(topTenCols)

cols = c('Title', 'Runtime', 'Gross', 'Domestic_Gross')
cols = c(cols, topTenCols)

df9 = df[cols]
df9.long = melt(df9, id.vars=c('Title', 'Runtime', 'Gross', 'Domestic_Gross'), variable.name='country')
df9.long = df9.long[apply(df9.long['value'],1,function(z) !any(z==0)),]

print(summary(df9.long))
print(head(df9.long))

ggplot(df9.long, aes(as.factor(country), Runtime)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")

ggplot(df9.long, aes(as.factor(country), Gross)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")

ggplot(df9.long, aes(as.factor(country), Domestic_Gross)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")

```

**Q**: Expected insight #1.

**A**:
First we plot runtime by country. While there is different distribution and sample size by country, notice that the median runtime by country is actually really similar. I would expect this because I am not aware of differences in runtime trends by country.

**Q**: Expected insight #2.

**A**:

Plotting the Gross Revenue by country, one expected insight is that lower income countries tend to have on average lower distribution of Gross Revenue.

## 10. Unexpected insight

Come up with one new insight (backed up by data and graphs) that is unexpected at first glance and do your best to motivate it. Same instructions apply as the previous task.

```{r}
# TODO: Find and illustrate one unexpected insight
df$made_to_dvd = as.numeric(!is.na(df$DVD))

ggplot(df, aes(imdbRating, made_to_dvd)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

**Q**: Unexpected insight.

**A**:
One thing that was unexpected to me was I would have expected that there would be a positive correlation between ratings and whether the move was made to a DVD (better movies would more likely be made into a DVD). The relationship I found was somewhat of an "inversed-U" shape. At very low ratings, the proportion of DVD's made is very low. Suprisingly, highly rated ratings also have lower made to DVD ratings.
